From 0a261158c6e67eb041ffb7ee98c047cb7f219157 Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Fri, 5 Dec 2025 18:26:43 -0800
Subject: [PATCH 01/15] gepa integration part 1

---
 eval_protocol/models.py                 |  31 +++-
 eval_protocol/pytest/evaluation_test.py |  35 ++++-
 eval_protocol/trainable_gepa_design.md  | 201 ++++++++++++++++++++++++
 eval_protocol/training/utils.py         |  19 +++
 tests/test_models.py                    |  32 ++++
 tests/test_training_utils.py            |  32 ++++
 6 files changed, 342 insertions(+), 8 deletions(-)
 create mode 100644 eval_protocol/trainable_gepa_design.md
 create mode 100644 eval_protocol/training/utils.py
 create mode 100644 tests/test_training_utils.py

diff --git a/eval_protocol/models.py b/eval_protocol/models.py
index 3ff0613e..911c13b9 100644
--- a/eval_protocol/models.py
+++ b/eval_protocol/models.py
@@ -3,7 +3,7 @@
 import importlib
 from datetime import datetime, timezone
 from enum import Enum
-from typing import Any, ClassVar, Dict, List, Literal, Optional, TypedDict, Union
+from typing import Any, ClassVar, Dict, List, Literal, Optional, TypedDict, Union, Callable, Sequence
 
 JSONType = Union[Dict[str, Any], List[Any], str, int, float, bool, None]
 
@@ -1190,3 +1190,32 @@ class MCPMultiClientConfiguration(BaseModel):
     """Represents a MCP configuration."""
 
     mcpServers: Dict[str, Union[MCPConfigurationServerStdio, MCPConfigurationServerUrl]]
+
+
+class EPParameters(BaseModel):
+    """The parameters of an `@evaluation_test`. Used for trainable integrations."""
+
+    completion_params: Any = None
+    input_messages: Any = None
+    input_dataset: Any = None
+    input_rows: Any = None
+    data_loaders: Any = None
+    dataset_adapter: Optional[Callable[..., Any]] = None
+    rollout_processor: Any = None
+    rollout_processor_kwargs: Dict[str, Any] | None = None
+    aggregation_method: Any = Field(default="mean")
+    passed_threshold: Any = None
+    disable_browser_open: bool = False
+    num_runs: int = 1
+    filtered_row_ids: Optional[Sequence[str]] = None
+    max_dataset_rows: Optional[int] = None
+    mcp_config_path: Optional[str] = None
+    max_concurrent_rollouts: int = 8
+    max_concurrent_evaluations: int = 64
+    server_script_path: Optional[str] = None
+    steps: int = 30
+    mode: Any = Field(default="pointwise")
+    combine_datasets: bool = True
+    preprocess_fn: Optional[Callable[[list[EvaluationRow]], list[EvaluationRow]]] = None
+    logger: Any = None
+    exception_handler_config: Any = None
diff --git a/eval_protocol/pytest/evaluation_test.py b/eval_protocol/pytest/evaluation_test.py
index f7fb16b3..82065517 100644
--- a/eval_protocol/pytest/evaluation_test.py
+++ b/eval_protocol/pytest/evaluation_test.py
@@ -21,6 +21,7 @@
     EvaluationThresholdDict,
     EvaluateResult,
     Status,
+    EPParameters,
 )
 from eval_protocol.pytest.dual_mode_wrapper import create_dual_mode_wrapper
 from eval_protocol.pytest.evaluation_test_postprocess import postprocess
@@ -695,13 +696,33 @@ async def _collect_result(config, lst):
         )
         pytest_wrapper = pytest.mark.asyncio(pytest_wrapper)
 
-        ep_params: dict[str, Any] = {
-            "rollout_processor": rollout_processor,
-            "server_script_path": server_script_path,
-            "mcp_config_path": mcp_config_path,
-            "rollout_processor_kwargs": rollout_processor_kwargs,
-            "mode": mode,
-        }
+        # Attach full evaluation parameter metadata for training integrations
+        ep_params: EPParameters = EPParameters(
+            completion_params=completion_params,
+            input_messages=input_messages,
+            input_dataset=input_dataset,
+            input_rows=input_rows,
+            data_loaders=data_loaders,
+            dataset_adapter=dataset_adapter,
+            rollout_processor=rollout_processor,
+            rollout_processor_kwargs=rollout_processor_kwargs,
+            aggregation_method=aggregation_method,
+            passed_threshold=passed_threshold,
+            disable_browser_open=disable_browser_open,
+            num_runs=num_runs,
+            filtered_row_ids=filtered_row_ids,
+            max_dataset_rows=max_dataset_rows,
+            mcp_config_path=mcp_config_path,
+            max_concurrent_rollouts=max_concurrent_rollouts,
+            max_concurrent_evaluations=max_concurrent_evaluations,
+            server_script_path=server_script_path,
+            steps=steps,
+            mode=mode,
+            combine_datasets=combine_datasets,
+            preprocess_fn=preprocess_fn,
+            logger=logger,
+            exception_handler_config=exception_handler_config,
+        )
 
         # Create the dual mode wrapper
         dual_mode_wrapper = create_dual_mode_wrapper(
diff --git a/eval_protocol/trainable_gepa_design.md b/eval_protocol/trainable_gepa_design.md
new file mode 100644
index 00000000..398840f8
--- /dev/null
+++ b/eval_protocol/trainable_gepa_design.md
@@ -0,0 +1,201 @@
+## GEPA-Trainable Interface Design for Eval Protocol
+
+### Goals
+
+- **Tunable prompts for existing benchmarks**: Allow benchmarks like `test_aime25.py` and `test_gpqa.py` to expose parts of their configuration (e.g., system prompts) as trainable parameters, without changing their core evaluation logic.
+- **Tight coupling with `@evaluation_test`**: Reuse the same rollout configuration, datasets, and metrics that are already defined via `evaluation_test`, instead of duplicating that configuration in a separate training API.
+- **GEPA as one optimizer backend**: Provide a clean integration point for GEPA (and potentially other optimizers later) without requiring benchmarks to depend on DSPy or GEPA directly.
+
+### High-Level Architecture
+
+- **Benchmark file (e.g., `test_aime25.py`)**
+  - Continues to define:
+    - Dataset adapter (`aime2025_dataset_adapter`).
+    - `@evaluation_test(...)`-decorated function (e.g., `test_aime25_pointwise`) that:
+      - Uses `SingleTurnRolloutProcessor` (or another processor).
+      - Computes per-row metrics and sets `row.evaluation_result`.
+  - Adds *optional* trainable wiring at the bottom, under `if __name__ == "__main__":`, that:
+    - Imports a trainable/core API from `eval_protocol.trainable`.
+    - Specifies what is tunable (e.g., the system prompt) and how to adapt rows using a candidate.
+    - Invokes a train routine (GEPA-based or otherwise).
+
+- **Trainable core**
+  - Provides a single central abstraction:
+    - **`EPParameters`**: Encapsulates everything `evaluation_test` knows about the eval in a structured form:
+      - One field for every parameter that `evaluation_test` accepts (dataset sources, adapters, completion params, rollout processor, aggregation, thresholds, etc.), after parsing/env overrides.
+    - **Candidate representation**: Start with `dict[str, str]` (e.g., `{"system_prompt": "..."}`), anticipating future extensions (few-shot examples, tool docs, etc.).
+  - Includes helper utilities to:
+    - Build an `EPParameters` instance by introspecting an `@evaluation_test`-decorated function.
+    - Run a single candidate or a batch of candidates through the full rollout + evaluation pipeline, returning aggregate scores (and optionally per-row scores).
+
+- **GEPA adapter (e.g., `eval_protocol/trainable/gepa_adapter.py`)**
+  - Wraps the trainable core and GEPA’s API:
+    - Accepts:
+      - An `EPConfig`.
+      - A candidate space definition (for now, implicit via `dict[str, str]` keys).
+      - GEPA configuration (budget, reflection model, seed, component selection strategy, etc.).
+    - Provides:
+      - A GEPA-compatible metric interface that:
+        - Given a candidate, uses `EPConfig` (and benchmark-specific logic such as a custom `dataset_adapter`) to:
+          - Construct or adapt rows for that candidate.
+          - Run rollouts (reusing the same processors and params as the test).
+          - Compute scalar scores (e.g., mean exact-match over a batch).
+      - A training routine that returns:
+        - A `best_candidate: dict[str, str]`.
+        - Optional rich result object (e.g., mapping to `GEPAResult`, additional stats).
+
+### Relationship to `evaluation_test` and `__ep_params__`
+
+- Existing `evaluation_test` code will attach:
+
+```python
+ep_params: dict[str, Any] = {
+    "rollout_processor": rollout_processor,
+    "server_script_path": server_script_path,
+    "mcp_config_path": mcp_config_path,
+    "rollout_processor_kwargs": rollout_processor_kwargs,
+    "mode": mode,
+}
+setattr(dual_mode_wrapper, "__ep_params__", ep_params)
+```
+
+- Design direction:
+  - **Use `__ep_params__` as the single source of truth**.
+  - **`__ep_params__` should contain all effective `evaluation_test` parameters**, including:
+    - Parsed `completion_params` (after env overrides).
+    - Dataset sources (`input_dataset`, `input_rows`, dataloaders, and `dataset_adapter`), after `parse_ep_*` transforms.
+    - `aggregation_method`, `num_runs`, `max_dataset_rows`, etc.
+    - Rollout and mode information (processor, kwargs, concurrency limits, mode).
+  - The trainable core can then **directly convert `__ep_params__` into an `EPParameters` instance** without maintaining a separate trainable-only config.
+
+- Trainable core will expose:
+  - A factory like:
+
+    ```python
+    def build_ep_parameters_from_test(
+        test_fn: TestFunction,
+    ) -> EPParameters:
+        ...
+    ```
+
+  - This function:
+    - Reads `test_fn.__ep_params__`.
+    - Reconstructs how to:
+      - Load and preprocess the dataset.
+      - Configure the rollout processor (`RolloutProcessorConfig`).
+      - Run rollouts and then apply the row-level metric (by calling the decorated test function in a library mode).
+
+- Training code (e.g., `python test_aime25.py`) then becomes:
+  - Import the test function (e.g., `test_aime25_pointwise`).
+  - Build an `EPParameters` from it.
+  - Call into a GEPA-based trainer that uses the `EPParameters`.
+
+### Open Questions
+
+- **Where tuned prompts live (storage format and location)**:
+  - GEPA already supports a `run_dir` for logging and checkpoints.
+  - We need to decide:
+    - Whether EP should:
+      - Treat `run_dir` as the canonical store and optionally add a small `best_candidate.json` there; or
+      - Provide an additional EP-level artifact format.
+  - For now, storage is left as an **explicit design TODO** and can be finalized once we have the core/adapter in place.
+
+### Work Split: Person A vs Person B
+
+#### Person A – Trainable Core & `evaluation_test` Integration
+
+- **1. Extend `evaluation_test` metadata (no behavior change)**
+  - Populate a single `__ep_config__` dict on the decorated test function that includes:
+    - Dataset specification (paths / input_rows / dataloaders, `dataset_adapter`, `max_dataset_rows`, etc.) after `parse_ep_*`.
+    - Parsed `completion_params` (after env overrides like `parse_ep_completion_params_overwrite`).
+    - Rollout settings (`rollout_processor`, `rollout_processor_kwargs`, `mode`, `max_concurrent_rollouts`, `max_concurrent_evaluations`).
+    - Aggregation and threshold metadata.
+  - Ensure:
+    - Backwards compatibility for existing tests.
+    - Clear typing and docstrings to guide future use.
+
+- **2. Define core trainable abstractions in `eval_protocol/trainable/core.py`**
+  - Define:
+    - `EPConfig`:
+      - A field for every parameter `evaluation_test` accepts (dataset, adapters, completion params, rollout processor, aggregation, thresholds, etc.).
+      - Can be serialized/inspected for external tooling.
+    - Candidate type alias (initially `Candidate = dict[str, str]`).
+  - Implement:
+    - `build_ep_config_from_test(test_fn: TestFunction) -> EPConfig`.
+      - Reads `__ep_config__`.
+      - Reuses the same dataset and rollout logic as pytest, but in a library-friendly way (no pytest invocation).
+  - Helper(s) to:
+    - Run a single candidate over the dataset, possibly with:
+      - A subset of rows (train vs val split initially determined by the benchmark or EPConfig).
+      - A configurable aggregation method (mean score to start).
+
+- **3. Minimal tests and documentation for the core**
+  - Add unit/integration tests that:
+    - Use a tiny fake `@evaluation_test` function.
+    - Confirm `build_ep_config_from_test` produces a config that can:
+      - Load mock rows.
+      - Run a dummy rollout processor.
+      - Apply a simple metric to produce scores.
+  - Document (in this design file or a short README) how benchmarks should think about exposing tunable pieces (e.g., via custom dataset adapters or other wiring).
+
+#### Person B – GEPA Adapter & Benchmark Wiring
+
+- **4. Implement GEPA integration in `eval_protocol/trainable/gepa_adapter.py`**
+  - Define a small adapter API, e.g.:
+
+```python
+class GEPATrainer:
+    def __init__(self, spec: TrainableBenchmarkSpec, inject_fn: InjectFn, ...gepa_config...):
+        ...
+
+    def train(self) -> tuple[Candidate, Any]:
+        """Run GEPA and return best candidate plus optional rich result."""
+```
+
+  - Inside, implement:
+    - Conversion from `(spec, inject_fn)` into a GEPA metric:
+      - For each candidate:
+        - Clone or map the base dataset rows, applying `inject_fn(candidate, row)`.
+        - Use the spec’s rollout runner + metric runner to compute per-example and aggregate scores.
+        - Return the aggregate score (and optional textual feedback) to GEPA.
+    - The call to `gepa.optimize(...)` with:
+      - `seed_candidate` constructed from the baseline configuration (e.g., default system prompt).
+      - Budget configuration (max metric calls / auto presets).
+      - Reflection config (reflection LM or other knobs) passed in via constructor.
+    - Mapping from `GEPAResult` (or equivalent) back into:
+      - `best_candidate: Candidate`.
+      - Optional rich result object (e.g., exposing Pareto-front stats).
+
+- **5. Wire a first benchmark: AIME 2025**
+  - In `eval_protocol/benchmarks/test_aime25.py`:
+    - Factor the row-scoring logic inside `test_aime25_pointwise` into a **reusable metric function** (pure function that sets `row.evaluation_result` given a rolled-out row).
+    - Decide how candidates should influence the evaluation:
+      - For example, by making the dataset adapter or message-construction logic candidate-aware (e.g., changing the system prompt).
+    - Add a `if __name__ == "__main__":` block that:
+      - Imports `test_aime25_pointwise` and builds an `EPConfig` via `build_ep_config_from_test`.
+      - Instantiates `GEPATrainer` with:
+        - The `EPConfig`.
+        - Initial GEPA config (budget, reflection model placeholder, seed).
+      - Calls `trainer.train()` and prints/logs the resulting `best_candidate` for now.
+    - Keep storage of tuned prompts as a TODO/extension point to be resolved later.
+
+- **6. Optional second benchmark: GPQA**
+  - Repeat step 5 for `test_gpqa.py`:
+    - Identify what’s tunable (system prompt, possibly chain-of-thought instructions).
+    - Extract metric logic into a reusable function.
+    - Add candidate-aware wiring (e.g., via dataset adapters) and an optional `__main__` entrypoint calling the same GEPA trainer.
+  - This will validate that:
+    - The abstractions generalize across tasks.
+    - No DSPy/GEPA-specific imports leak into benchmark files (other than a small, well-defined trainable API).
+
+### Coordination Notes
+
+- **Order of work**
+  - Person A should go first (or in parallel up to the point where `EPConfig` and `build_ep_config_from_test` are usable).
+  - Person B can stub against interfaces and adjust once Person A’s core is available.
+- **Integration checkpoints**
+  - After Person A lands the core + tests:
+    - Person B wires AIME with a very simple “optimizer” (even random search) to smoke-test the path before hooking up real GEPA.
+  - After GEPA integration works for AIME:
+    - Decide on the canonical way to treat GEPA’s `run_dir` and/or additional artifacts for tuned prompts.
+    - Optionally add a small helper that knows how to “run evaluation once with best GEPA candidate” for CI workflows.
diff --git a/eval_protocol/training/utils.py b/eval_protocol/training/utils.py
new file mode 100644
index 00000000..10457aa0
--- /dev/null
+++ b/eval_protocol/training/utils.py
@@ -0,0 +1,19 @@
+from typing import Any
+
+from eval_protocol.models import EPParameters
+
+
+def build_ep_parameters_from_test(test_fn: Any) -> EPParameters:
+    """
+    Build an `EPParameters` instance from an `@evaluation_test`-decorated function.
+
+    The decorator is responsible for attaching a `__ep_params__` attribute that
+    contains all effective evaluation parameters after parsing/env overrides.
+    """
+    if not hasattr(test_fn, "__ep_params__"):
+        raise ValueError(
+            "The provided test function does not have `__ep_params__` attached. "
+            "Ensure it is decorated with `@evaluation_test` from eval_protocol.pytest."
+        )
+
+    return getattr(test_fn, "__ep_params__")
diff --git a/tests/test_models.py b/tests/test_models.py
index 723685b8..27529829 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -11,6 +11,7 @@
     Message,
     MetricResult,
     StepOutput,
+    EPParameters,
 )
 
 
@@ -721,3 +722,34 @@ def test_message_dump_for_chat_completion_request():
     assert "weight" not in dictionary
     assert "reasoning_content" not in dictionary
     assert dictionary["content"] == "Hello, how are you?"
+
+
+def test_ep_parameters_defaults():
+    """EPParameters should have sensible defaults for core fields."""
+    params = EPParameters()
+
+    assert params.completion_params is None
+    assert params.num_runs == 1
+    assert params.disable_browser_open is False
+    assert params.max_concurrent_rollouts == 8
+    assert params.max_concurrent_evaluations == 64
+    assert params.mode == "pointwise"
+    assert params.combine_datasets is True
+
+
+def test_ep_parameters_accepts_arbitrary_types():
+    """EPParameters should allow rich Python types for callable/logger fields."""
+
+    def dummy_preprocess(rows):
+        return rows
+
+    def dummy_adapter(*args, **kwargs):
+        return None
+
+    logger = logging.getLogger("ep-params-test")
+
+    params = EPParameters(dataset_adapter=dummy_adapter, preprocess_fn=dummy_preprocess, logger=logger)
+
+    assert params.dataset_adapter is dummy_adapter
+    assert params.preprocess_fn is dummy_preprocess
+    assert params.logger is logger
diff --git a/tests/test_training_utils.py b/tests/test_training_utils.py
new file mode 100644
index 00000000..084ff9a9
--- /dev/null
+++ b/tests/test_training_utils.py
@@ -0,0 +1,32 @@
+import pytest
+
+from eval_protocol.models import EPParameters
+from eval_protocol.training.utils import build_ep_parameters_from_test
+
+
+def test_build_ep_parameters_from_test_returns_attached_model():
+    """build_ep_parameters_from_test should return the EPParameters attached to the test function."""
+
+    def dummy_test() -> None:
+        pass
+
+    params = EPParameters(num_runs=3, completion_params={"model": "gpt-4"})
+    setattr(dummy_test, "__ep_params__", params)
+
+    result = build_ep_parameters_from_test(dummy_test)
+
+    assert result is params
+    assert result.num_runs == 3
+    assert result.completion_params == {"model": "gpt-4"}
+
+
+def test_build_ep_parameters_from_test_missing_attr_raises():
+    """build_ep_parameters_from_test should raise when __ep_params__ is missing."""
+
+    def dummy_test_no_attr() -> None:
+        pass
+
+    with pytest.raises(ValueError) as exc_info:
+        build_ep_parameters_from_test(dummy_test_no_attr)
+
+    assert "__ep_params__" in str(exc_info.value)

From 42e0b0889c9888b1c6e736593516f39aa388777d Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Fri, 5 Dec 2025 18:29:49 -0800
Subject: [PATCH 02/15] update

---
 eval_protocol/trainable_gepa_design.md | 30 +++++++++++++-------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/eval_protocol/trainable_gepa_design.md b/eval_protocol/trainable_gepa_design.md
index 398840f8..5859fba4 100644
--- a/eval_protocol/trainable_gepa_design.md
+++ b/eval_protocol/trainable_gepa_design.md
@@ -1,8 +1,8 @@
-## GEPA-Trainable Interface Design for Eval Protocol
+## GEPA-training Interface Design for Eval Protocol
 
 ### Goals
 
-- **Tunable prompts for existing benchmarks**: Allow benchmarks like `test_aime25.py` and `test_gpqa.py` to expose parts of their configuration (e.g., system prompts) as trainable parameters, without changing their core evaluation logic.
+- **Tunable prompts for existing benchmarks**: Allow benchmarks like `test_aime25.py` and `test_gpqa.py` to expose parts of their configuration (e.g., system prompts) as training parameters, without changing their core evaluation logic.
 - **Tight coupling with `@evaluation_test`**: Reuse the same rollout configuration, datasets, and metrics that are already defined via `evaluation_test`, instead of duplicating that configuration in a separate training API.
 - **GEPA as one optimizer backend**: Provide a clean integration point for GEPA (and potentially other optimizers later) without requiring benchmarks to depend on DSPy or GEPA directly.
 
@@ -14,12 +14,12 @@
     - `@evaluation_test(...)`-decorated function (e.g., `test_aime25_pointwise`) that:
       - Uses `SingleTurnRolloutProcessor` (or another processor).
       - Computes per-row metrics and sets `row.evaluation_result`.
-  - Adds *optional* trainable wiring at the bottom, under `if __name__ == "__main__":`, that:
-    - Imports a trainable/core API from `eval_protocol.trainable`.
+  - Adds *optional* training wiring at the bottom, under `if __name__ == "__main__":`, that:
+    - Imports a training/core API from `eval_protocol.training`.
     - Specifies what is tunable (e.g., the system prompt) and how to adapt rows using a candidate.
     - Invokes a train routine (GEPA-based or otherwise).
 
-- **Trainable core**
+- **training core**
   - Provides a single central abstraction:
     - **`EPParameters`**: Encapsulates everything `evaluation_test` knows about the eval in a structured form:
       - One field for every parameter that `evaluation_test` accepts (dataset sources, adapters, completion params, rollout processor, aggregation, thresholds, etc.), after parsing/env overrides.
@@ -28,8 +28,8 @@
     - Build an `EPParameters` instance by introspecting an `@evaluation_test`-decorated function.
     - Run a single candidate or a batch of candidates through the full rollout + evaluation pipeline, returning aggregate scores (and optionally per-row scores).
 
-- **GEPA adapter (e.g., `eval_protocol/trainable/gepa_adapter.py`)**
-  - Wraps the trainable core and GEPA’s API:
+- **GEPA adapter (e.g., `eval_protocol/training/gepa_adapter.py`)**
+  - Wraps the training core and GEPA’s API:
     - Accepts:
       - An `EPConfig`.
       - A candidate space definition (for now, implicit via `dict[str, str]` keys).
@@ -66,9 +66,9 @@ setattr(dual_mode_wrapper, "__ep_params__", ep_params)
     - Dataset sources (`input_dataset`, `input_rows`, dataloaders, and `dataset_adapter`), after `parse_ep_*` transforms.
     - `aggregation_method`, `num_runs`, `max_dataset_rows`, etc.
     - Rollout and mode information (processor, kwargs, concurrency limits, mode).
-  - The trainable core can then **directly convert `__ep_params__` into an `EPParameters` instance** without maintaining a separate trainable-only config.
+  - The training core can then **directly convert `__ep_params__` into an `EPParameters` instance** without maintaining a separate training-only config.
 
-- Trainable core will expose:
+- training core will expose:
   - A factory like:
 
     ```python
@@ -90,7 +90,7 @@ setattr(dual_mode_wrapper, "__ep_params__", ep_params)
   - Build an `EPParameters` from it.
   - Call into a GEPA-based trainer that uses the `EPParameters`.
 
-### Open Questions
+### TODO for derek to figure out: how to store the changing system prompts.
 
 - **Where tuned prompts live (storage format and location)**:
   - GEPA already supports a `run_dir` for logging and checkpoints.
@@ -102,7 +102,7 @@ setattr(dual_mode_wrapper, "__ep_params__", ep_params)
 
 ### Work Split: Person A vs Person B
 
-#### Person A – Trainable Core & `evaluation_test` Integration
+#### Person A – training Core & `evaluation_test` Integration
 
 - **1. Extend `evaluation_test` metadata (no behavior change)**
   - Populate a single `__ep_config__` dict on the decorated test function that includes:
@@ -114,7 +114,7 @@ setattr(dual_mode_wrapper, "__ep_params__", ep_params)
     - Backwards compatibility for existing tests.
     - Clear typing and docstrings to guide future use.
 
-- **2. Define core trainable abstractions in `eval_protocol/trainable/core.py`**
+- **2. Define core training abstractions in `eval_protocol/training/core.py`**
   - Define:
     - `EPConfig`:
       - A field for every parameter `evaluation_test` accepts (dataset, adapters, completion params, rollout processor, aggregation, thresholds, etc.).
@@ -140,12 +140,12 @@ setattr(dual_mode_wrapper, "__ep_params__", ep_params)
 
 #### Person B – GEPA Adapter & Benchmark Wiring
 
-- **4. Implement GEPA integration in `eval_protocol/trainable/gepa_adapter.py`**
+- **4. Implement GEPA integration in `eval_protocol/training/gepa_adapter.py`**
   - Define a small adapter API, e.g.:
 
 ```python
 class GEPATrainer:
-    def __init__(self, spec: TrainableBenchmarkSpec, inject_fn: InjectFn, ...gepa_config...):
+    def __init__(self, spec: trainingBenchmarkSpec, inject_fn: InjectFn, ...gepa_config...):
         ...
 
     def train(self) -> tuple[Candidate, Any]:
@@ -186,7 +186,7 @@ class GEPATrainer:
     - Add candidate-aware wiring (e.g., via dataset adapters) and an optional `__main__` entrypoint calling the same GEPA trainer.
   - This will validate that:
     - The abstractions generalize across tasks.
-    - No DSPy/GEPA-specific imports leak into benchmark files (other than a small, well-defined trainable API).
+    - No DSPy/GEPA-specific imports leak into benchmark files (other than a small, well-defined training API).
 
 ### Coordination Notes
 

From 4fa4162b6588978d2222b85cb0fc6e9f5db32c51 Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Sat, 6 Dec 2025 00:13:07 -0800
Subject: [PATCH 03/15] skeleton of gepa trainer

---
 eval_protocol/benchmarks/test_aime25.py |  16 +++
 eval_protocol/trainable_gepa_design.md  |  39 ++++++-
 eval_protocol/training/__init__.py      |   3 +
 eval_protocol/training/gepa_adapter.py  | 138 ++++++++++++++++++++++++
 eval_protocol/training/gepa_utils.py    |  32 ++++++
 pyproject.toml                          |   1 +
 6 files changed, 227 insertions(+), 2 deletions(-)
 create mode 100644 eval_protocol/training/__init__.py
 create mode 100644 eval_protocol/training/gepa_adapter.py
 create mode 100644 eval_protocol/training/gepa_utils.py

diff --git a/eval_protocol/benchmarks/test_aime25.py b/eval_protocol/benchmarks/test_aime25.py
index 6eb785a7..debd9fad 100644
--- a/eval_protocol/benchmarks/test_aime25.py
+++ b/eval_protocol/benchmarks/test_aime25.py
@@ -12,6 +12,8 @@
     SingleTurnRolloutProcessor,
 )
 from eval_protocol.pytest.evaluation_test import evaluation_test
+from eval_protocol.training import GEPATrainer
+from eval_protocol.training.gepa_utils import build_reflection_lm
 
 SYSTEM_PROMPT = (
     "You are a helpful math assistant. Please reason step by step, and put your final answer within \\boxed{...}."
@@ -131,3 +133,17 @@ def test_aime25_pointwise(row: EvaluationRow) -> EvaluationRow:
         metrics=metrics,
     )
     return row
+
+
+if __name__ == "__main__":
+    trainer = GEPATrainer(test_aime25_pointwise)
+    reflection_lm = build_reflection_lm("gpt-5")
+
+    optimized_program = trainer.train(
+        num_threads=32,
+        track_stats=True,
+        reflection_minibatch_size=3,
+        reflection_lm=reflection_lm,
+    )
+
+    print(trainer.evaluate(optimized_program))
diff --git a/eval_protocol/trainable_gepa_design.md b/eval_protocol/trainable_gepa_design.md
index 5859fba4..b66fb7e0 100644
--- a/eval_protocol/trainable_gepa_design.md
+++ b/eval_protocol/trainable_gepa_design.md
@@ -19,7 +19,7 @@
     - Specifies what is tunable (e.g., the system prompt) and how to adapt rows using a candidate.
     - Invokes a train routine (GEPA-based or otherwise).
 
-- **training core**
+- **Training core**
   - Provides a single central abstraction:
     - **`EPParameters`**: Encapsulates everything `evaluation_test` knows about the eval in a structured form:
       - One field for every parameter that `evaluation_test` accepts (dataset sources, adapters, completion params, rollout processor, aggregation, thresholds, etc.), after parsing/env overrides.
@@ -68,7 +68,7 @@ setattr(dual_mode_wrapper, "__ep_params__", ep_params)
     - Rollout and mode information (processor, kwargs, concurrency limits, mode).
   - The training core can then **directly convert `__ep_params__` into an `EPParameters` instance** without maintaining a separate training-only config.
 
-- training core will expose:
+- Training core will expose:
   - A factory like:
 
     ```python
@@ -199,3 +199,38 @@ class GEPATrainer:
   - After GEPA integration works for AIME:
     - Decide on the canonical way to treat GEPA’s `run_dir` and/or additional artifacts for tuned prompts.
     - Optionally add a small helper that knows how to “run evaluation once with best GEPA candidate” for CI workflows.
+
+
+future:
+
+this is how gepa defines eval:
+
+def metric(
+    gold: Example,
+    pred: Prediction,
+    trace: Optional[DSPyTrace] = None,
+    pred_name: Optional[str] = None,
+    pred_trace: Optional[DSPyTrace] = None,
+) -> float | ScoreWithFeedback:
+    """
+    This function is called with the following arguments:
+    - gold: The gold example.
+    - pred: The predicted output.
+    - trace: Optional. The trace of the program's execution.
+    - pred_name: Optional. The name of the target predictor currently being optimized by GEPA, for which
+        the feedback is being requested.
+    - pred_trace: Optional. The trace of the target predictor's execution GEPA is seeking feedback for.
+
+    Note the `pred_name` and `pred_trace` arguments. During optimization, GEPA will call the metric to obtain
+    feedback for individual predictors being optimized. GEPA provides the name of the predictor in `pred_name`
+    and the sub-trace (of the trace) corresponding to the predictor in `pred_trace`.
+    If available at the predictor level, the metric should return {'score': float, 'feedback': str} corresponding
+    to the predictor.
+    If not available at the predictor level, the metric can also return a text feedback at the program level
+    (using just the gold, pred and trace).
+    If no feedback is returned, GEPA will use a simple text feedback consisting of just the score:
+    f"This trajectory got a score of {score}."
+    """
+    ...
+
+ideally generic way to turn evaluation_test into this.
diff --git a/eval_protocol/training/__init__.py b/eval_protocol/training/__init__.py
new file mode 100644
index 00000000..998c5e9c
--- /dev/null
+++ b/eval_protocol/training/__init__.py
@@ -0,0 +1,3 @@
+from gepa_adapter import GEPATrainer
+
+__all__ = ["GEPATrainer"]
diff --git a/eval_protocol/training/gepa_adapter.py b/eval_protocol/training/gepa_adapter.py
new file mode 100644
index 00000000..47b35267
--- /dev/null
+++ b/eval_protocol/training/gepa_adapter.py
@@ -0,0 +1,138 @@
+from typing import Any, Dict, Literal
+
+import dspy
+from dspy.clients.lm import LM
+from dspy.primitives import Module
+from dspy.teleprompt.gepa.gepa import GEPA
+from gepa.core.adapter import ProposalFn
+from gepa.proposer.reflective_mutation.base import ReflectionComponentSelector
+
+from eval_protocol.models import EPParameters, EvaluationRow
+from eval_protocol.pytest.types import TestFunction
+from eval_protocol.training.gepa_utils import REFLECTION_LM_CONFIGS
+from eval_protocol.training.utils import build_ep_parameters_from_test
+
+
+class GEPATrainer:
+    """
+    High-level entrypoint for running GEPA-style training against an existing
+    `@evaluation_test`-decorated function.
+
+    This class is intentionally minimal for now:
+    - It captures `EPParameters` from the provided test function via
+      `build_ep_parameters_from_test`.
+    - It stores any GEPA-related configuration kwargs for future use.
+    - The actual GEPA optimization loop is left as a TODO.
+    """
+
+    def __init__(self, test_fn: TestFunction) -> None:
+        """
+        Args:
+            test_fn: The `@evaluation_test`-decorated function defining the eval.
+        """
+        self.test_fn = test_fn
+        self.ep_params: EPParameters = build_ep_parameters_from_test(test_fn)
+
+        self.metric = (
+            test_fn  # TODO: need to convert our ep test_fn to a GEPA metric. also need to inject the feedback text.
+        )
+
+        self.program = ...  # TODO: converting between a program (dspy.Module) and an @evaluation_test is a bit tricky.
+
+        self.train_set, self.val_set, self.test_set = (
+            ...,
+            ...,
+            ...,
+        )  # TODO: need to convert our input_dataset to a train set
+
+    def train(
+        self,
+        auto: Literal["light", "medium", "heavy"] | None = None,
+        max_full_evals: int | None = None,
+        max_metric_calls: int | None = None,
+        reflection_minibatch_size: int = 3,
+        candidate_selection_strategy: Literal["pareto", "current_best"] = "pareto",
+        reflection_lm: LM | None = None,
+        skip_perfect_score: bool = True,
+        add_format_failure_as_feedback: bool = False,
+        instruction_proposer: ProposalFn | None = None,
+        component_selector: ReflectionComponentSelector | str = "round_robin",
+        use_merge: bool = True,
+        max_merge_invocations: int | None = 5,
+        num_threads: int | None = None,
+        failure_score: float = 0.0,
+        perfect_score: float = 1.0,
+        log_dir: str | None = None,
+        track_stats: bool = False,
+        use_wandb: bool = False,
+        wandb_api_key: str | None = None,
+        wandb_init_kwargs: dict[str, Any] | None = None,
+        track_best_outputs: bool = False,
+        warn_on_score_mismatch: bool = True,
+        enable_tool_optimization: bool = False,
+        use_mlflow: bool = False,
+        seed: int | None = 0,
+        gepa_kwargs: dict | None = None,
+    ) -> Module:
+        """
+        Run GEPA to optimize over candidates.
+        """
+        gepa_args: dict[str, Any] = {
+            "auto": auto,
+            "max_full_evals": max_full_evals,
+            "max_metric_calls": max_metric_calls,
+            "reflection_minibatch_size": reflection_minibatch_size,
+            "candidate_selection_strategy": candidate_selection_strategy,
+            "reflection_lm": reflection_lm,
+            "skip_perfect_score": skip_perfect_score,
+            "add_format_failure_as_feedback": add_format_failure_as_feedback,
+            "instruction_proposer": instruction_proposer,
+            "component_selector": component_selector,
+            "use_merge": use_merge,
+            "max_merge_invocations": max_merge_invocations,
+            "num_threads": num_threads,
+            "failure_score": failure_score,
+            "perfect_score": perfect_score,
+            "log_dir": log_dir,
+            "track_stats": track_stats,
+            "use_wandb": use_wandb,
+            "wandb_api_key": wandb_api_key,
+            "wandb_init_kwargs": wandb_init_kwargs,
+            "track_best_outputs": track_best_outputs,
+            "warn_on_score_mismatch": warn_on_score_mismatch,
+            "enable_tool_optimization": enable_tool_optimization,
+            "use_mlflow": use_mlflow,
+            "seed": seed,
+        }
+        gepa_args.update(gepa_kwargs or {})
+
+        optimizer = GEPA(
+            metric=self.metric,
+            **gepa_args,
+        )
+
+        optimized_program = optimizer.compile(
+            self.program,
+            trainset=self.train_set,
+            valset=self.val_set,
+        )
+
+        return optimized_program
+
+    def evaluate(self, optimized_program: Module) -> list[EvaluationRow]:
+        # convert back to EP
+
+        # and then just run our evaluation_test function on the optimized program.
+
+        # OR we can evaluate using dspy.Evaluate
+
+        # evaluate = dspy.Evaluate(
+        #     devset=self.test_set,
+        #     metric=self.metric,
+        #     num_threads=32,
+        #     display_table=True,
+        #     display_progress=True
+        # )
+
+        # return evaluate(self.optimized_program)
+        ...
diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
new file mode 100644
index 00000000..520c7de4
--- /dev/null
+++ b/eval_protocol/training/gepa_utils.py
@@ -0,0 +1,32 @@
+import os
+
+import dspy
+from dspy.clients.lm import LM
+
+REFLECTION_LM_CONFIGS = {
+    "gpt-5": {
+        "model": "gpt-5",
+        "temperature": 1.0,
+        "max_tokens": 32000,
+        "api_key": os.getenv("OPENAI_API_KEY"),
+        "base_url": "https://api.openai.com/v1",
+    },
+    "kimi-k2-instruct-0905": {
+        "model": "accounts/fireworks/models/kimi-k2-instruct-0905",
+        "temperature": 0.6,  # Kimi recommended temperature
+        "max_tokens": 131000,
+        "api_key": os.getenv("FIREWORKS_API_KEY"),
+        "base_url": "https://api.fireworks.ai/inference/v1",
+    },
+}
+
+
+def build_reflection_lm(reflection_lm_name: str) -> LM:
+    reflection_lm_config = REFLECTION_LM_CONFIGS[reflection_lm_name]
+    return dspy.LM(
+        model=reflection_lm_config["model"],
+        temperature=reflection_lm_config["temperature"],
+        max_tokens=reflection_lm_config["max_tokens"],
+        api_key=reflection_lm_config["api_key"],
+        base_url=reflection_lm_config["base_url"],
+    )
diff --git a/pyproject.toml b/pyproject.toml
index a43f773a..ceea22cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,6 +47,7 @@ dependencies = [
     "deepdiff>=6.0.0",
     "websockets>=15.0.1",
     "fastapi>=0.116.1",
+    "dspy>=3.0.0",
 ]
 
 [project.urls]

From d6eb57844be5c06376faacad7b3f02331737c689 Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Sat, 6 Dec 2025 00:19:23 -0800
Subject: [PATCH 04/15] abc trainer

---
 eval_protocol/training/__init__.py               |  2 +-
 .../{gepa_adapter.py => gepa_trainer.py}         |  6 +++---
 eval_protocol/training/trainer.py                | 16 ++++++++++++++++
 3 files changed, 20 insertions(+), 4 deletions(-)
 rename eval_protocol/training/{gepa_adapter.py => gepa_trainer.py} (97%)
 create mode 100644 eval_protocol/training/trainer.py

diff --git a/eval_protocol/training/__init__.py b/eval_protocol/training/__init__.py
index 998c5e9c..fcb904c1 100644
--- a/eval_protocol/training/__init__.py
+++ b/eval_protocol/training/__init__.py
@@ -1,3 +1,3 @@
-from gepa_adapter import GEPATrainer
+from gepa_trainer import GEPATrainer
 
 __all__ = ["GEPATrainer"]
diff --git a/eval_protocol/training/gepa_adapter.py b/eval_protocol/training/gepa_trainer.py
similarity index 97%
rename from eval_protocol/training/gepa_adapter.py
rename to eval_protocol/training/gepa_trainer.py
index 47b35267..b2956ce1 100644
--- a/eval_protocol/training/gepa_adapter.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -9,11 +9,11 @@
 
 from eval_protocol.models import EPParameters, EvaluationRow
 from eval_protocol.pytest.types import TestFunction
-from eval_protocol.training.gepa_utils import REFLECTION_LM_CONFIGS
+from eval_protocol.training.trainer import Trainer
 from eval_protocol.training.utils import build_ep_parameters_from_test
 
 
-class GEPATrainer:
+class GEPATrainer(Trainer):
     """
     High-level entrypoint for running GEPA-style training against an existing
     `@evaluation_test`-decorated function.
@@ -30,7 +30,7 @@ def __init__(self, test_fn: TestFunction) -> None:
         Args:
             test_fn: The `@evaluation_test`-decorated function defining the eval.
         """
-        self.test_fn = test_fn
+        super().__init__(test_fn)
         self.ep_params: EPParameters = build_ep_parameters_from_test(test_fn)
 
         self.metric = (
diff --git a/eval_protocol/training/trainer.py b/eval_protocol/training/trainer.py
new file mode 100644
index 00000000..1008bb41
--- /dev/null
+++ b/eval_protocol/training/trainer.py
@@ -0,0 +1,16 @@
+from abc import ABC, abstractmethod
+
+from eval_protocol.pytest.types import TestFunction
+
+
+class Trainer(ABC):
+    def __init__(self, test_fn: TestFunction):
+        self.test_fn = test_fn
+
+    @abstractmethod
+    def train(self, *args, **kwargs): ...
+
+    @abstractmethod
+    def evaluate(self, *args, **kwargs):
+        # evaluation logic possibly can be shared since it's EP. TBD
+        ...

From 9ef49a0d93bfe6214befaec1b1802d3a11808700 Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Sat, 6 Dec 2025 00:20:38 -0800
Subject: [PATCH 05/15] assign

---
 eval_protocol/training/gepa_trainer.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/eval_protocol/training/gepa_trainer.py b/eval_protocol/training/gepa_trainer.py
index b2956ce1..8c05f824 100644
--- a/eval_protocol/training/gepa_trainer.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -33,17 +33,17 @@ def __init__(self, test_fn: TestFunction) -> None:
         super().__init__(test_fn)
         self.ep_params: EPParameters = build_ep_parameters_from_test(test_fn)
 
-        self.metric = (
-            test_fn  # TODO: need to convert our ep test_fn to a GEPA metric. also need to inject the feedback text.
-        )
+        self.metric = test_fn  # TODO @derek. need to convert our ep test_fn to a GEPA metric. also need to inject the feedback text.
 
-        self.program = ...  # TODO: converting between a program (dspy.Module) and an @evaluation_test is a bit tricky.
+        self.program = (
+            ...
+        )  # TODO @shreymodi1: converting between a program (dspy.Module) and an @evaluation_test is a bit tricky.
 
         self.train_set, self.val_set, self.test_set = (
             ...,
             ...,
             ...,
-        )  # TODO: need to convert our input_dataset to a train set
+        )  # TODO @shreymodi1. need to convert our input_dataset to a train set
 
     def train(
         self,

From c61de5bd77083aa4fdb81eeb430aeb69890fe2cb Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Sat, 6 Dec 2025 00:21:59 -0800
Subject: [PATCH 06/15] fix lock

---
 uv.lock | 251 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 251 insertions(+)

diff --git a/uv.lock b/uv.lock
index 38b07c4a..2a7ae8f0 100644
--- a/uv.lock
+++ b/uv.lock
@@ -187,6 +187,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f5/10/6c25ed6de94c49f88a91fa5018cb4c0f3625f31d5be9f771ebe5cc7cd506/aiosqlite-0.21.0-py3-none-any.whl", hash = "sha256:2549cf4057f95f53dcba16f2b64e8e2791d7e1adedb13197dd8ed77bb226d7d0", size = 15792, upload-time = "2025-02-03T07:30:13.6Z" },
 ]
 
+[[package]]
+name = "alembic"
+version = "1.17.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mako" },
+    { name = "sqlalchemy" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = "sha256:bbe9751705c5e0f14877f02d46c53d10885e377e3d90eda810a016f9baa19e8e", size = 1988064, upload-time = "2025-11-14T20:35:04.057Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/88/6237e97e3385b57b5f1528647addea5cc03d4d65d5979ab24327d41fb00d/alembic-1.17.2-py3-none-any.whl", hash = "sha256:f483dd1fe93f6c5d49217055e4d15b905b425b6af906746abb35b69c1996c4e6", size = 248554, upload-time = "2025-11-14T20:35:05.699Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -329,6 +344,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028", size = 5721, upload-time = "2023-08-10T16:35:55.203Z" },
 ]
 
+[[package]]
+name = "asyncer"
+version = "0.0.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ff/67/7ea59c3e69eaeee42e7fc91a5be67ca5849c8979acac2b920249760c6af2/asyncer-0.0.8.tar.gz", hash = "sha256:a589d980f57e20efb07ed91d0dbe67f1d2fd343e7142c66d3a099f05c620739c", size = 18217, upload-time = "2024-08-24T23:15:36.449Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8a/04/15b6ca6b7842eda2748bda0a0af73f2d054e9344320f8bba01f994294bcb/asyncer-0.0.8-py3-none-any.whl", hash = "sha256:5920d48fc99c8f8f0f1576e1882f5022885589c5fcbc46ce4224ec3e53776eeb", size = 9209, upload-time = "2024-08-24T23:15:35.317Z" },
+]
+
 [[package]]
 name = "asyncstdlib-fw"
 version = "3.13.2"
@@ -830,6 +857,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "colorlog"
+version = "6.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162, upload-time = "2025-10-16T16:14:11.978Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743, upload-time = "2025-10-16T16:14:10.512Z" },
+]
+
 [[package]]
 name = "comm"
 version = "0.2.3"
@@ -1118,6 +1157,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c9/7a/cef76fd8438a42f96db64ddaa85280485a9c395e7df3db8158cfec1eee34/dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7", size = 116252, upload-time = "2024-01-27T23:42:14.239Z" },
 ]
 
+[[package]]
+name = "diskcache"
+version = "5.6.3"
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+]
+
 [[package]]
 name = "sse-starlette"
 version = "2.4.1"

From 693274e67139578cb3ff9e70fe5d601bf425fd2d Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Sat, 6 Dec 2025 00:53:50 -0800
Subject: [PATCH 07/15] attempt at primitive conversion

---
 eval_protocol/benchmarks/test_aime25.py | 48 +++++++++++++++-
 eval_protocol/training/gepa_trainer.py  |  7 +--
 eval_protocol/training/gepa_utils.py    | 75 +++++++++++++++++++++++++
 3 files changed, 125 insertions(+), 5 deletions(-)

diff --git a/eval_protocol/benchmarks/test_aime25.py b/eval_protocol/benchmarks/test_aime25.py
index debd9fad..c921cef7 100644
--- a/eval_protocol/benchmarks/test_aime25.py
+++ b/eval_protocol/benchmarks/test_aime25.py
@@ -63,6 +63,44 @@ def _normalize_to_int_or_none(s: Optional[str]) -> Optional[int]:
         return None
 
 
+def _build_feedback_text(
+    *,
+    extracted_int: Optional[int],
+    gt_int: Optional[int],
+    is_valid: bool,
+    raw_model_answer: str,
+    ground_truth: Optional[str],
+) -> str:
+    """
+    Build a feedback string similar in spirit to the GEPA `metric_with_feedback`.
+
+    Cases:
+    - Parse failure (model or gold): explain integer formatting and show correct answer.
+    - Correct: "Your answer is correct. The correct answer is '...'."
+    - Incorrect: "Your answer is incorrect. The correct answer is '...'."
+    """
+    correct_answer_display = str(gt_int if gt_int is not None else (ground_truth or ""))
+
+    if not is_valid:
+        # Could not parse either the model answer or the gold answer as an integer.
+        feedback_text = (
+            "The final answer must be a valid integer and nothing else. "
+            f"You responded with '{raw_model_answer}', which couldn't be parsed as a python integer. "
+            "Please ensure your answer is a valid integer without any additional text or formatting."
+        )
+        if correct_answer_display:
+            feedback_text += f" The correct answer is '{correct_answer_display}'."
+        return feedback_text
+
+    if extracted_int == gt_int:
+        return f"Your answer is correct. The correct answer is '{correct_answer_display}'."
+    else:
+        return f"Your answer is incorrect. The correct answer is '{correct_answer_display}'."
+
+    # TODO: our dataset does not contain written solutions, so we cannot provide feedback on the solution. maybe need to add it later.
+    # they're using https://huggingface.co/datasets/AI-MO/aimo-validation-aime
+
+
 def aime2025_dataset_adapter(rows: List[Dict[str, Any]]) -> List[EvaluationRow]:
     converted: List[EvaluationRow] = []
     for r in rows:
@@ -126,9 +164,17 @@ def test_aime25_pointwise(row: EvaluationRow) -> EvaluationRow:
         )
     }
 
+    feedback_text = _build_feedback_text(
+        extracted_int=extracted_int,
+        gt_int=gt_int,
+        is_valid=is_valid,
+        raw_model_answer=content_str,
+        ground_truth=str(row.ground_truth),
+    )
+
     row.evaluation_result = EvaluateResult(
         score=score,
-        reason=("Answer correct" if score == 1.0 else "Answer incorrect"),
+        reason=feedback_text,
         is_score_valid=is_valid,
         metrics=metrics,
     )
diff --git a/eval_protocol/training/gepa_trainer.py b/eval_protocol/training/gepa_trainer.py
index 8c05f824..6fc2a08a 100644
--- a/eval_protocol/training/gepa_trainer.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -11,6 +11,7 @@
 from eval_protocol.pytest.types import TestFunction
 from eval_protocol.training.trainer import Trainer
 from eval_protocol.training.utils import build_ep_parameters_from_test
+from eval_protocol.training.gepa_utils import ep_test_to_gepa_metric
 
 
 class GEPATrainer(Trainer):
@@ -33,11 +34,9 @@ def __init__(self, test_fn: TestFunction) -> None:
         super().__init__(test_fn)
         self.ep_params: EPParameters = build_ep_parameters_from_test(test_fn)
 
-        self.metric = test_fn  # TODO @derek. need to convert our ep test_fn to a GEPA metric. also need to inject the feedback text.
+        self.metric = ep_test_to_gepa_metric(test_fn)
 
-        self.program = (
-            ...
-        )  # TODO @shreymodi1: converting between a program (dspy.Module) and an @evaluation_test is a bit tricky.
+        self.program = ...  # TODO @shreymodi1: converting between a program (dspy.Module) and rollout processors is a bit tricky. maybe start with single turn
 
         self.train_set, self.val_set, self.test_set = (
             ...,
diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
index 520c7de4..ec50245c 100644
--- a/eval_protocol/training/gepa_utils.py
+++ b/eval_protocol/training/gepa_utils.py
@@ -1,7 +1,15 @@
 import os
+from typing import Optional
 
 import dspy
 from dspy.clients.lm import LM
+from dspy.primitives import Example, Prediction
+from dspy.teleprompt.gepa.gepa_utils import DSPyTrace, ScoreWithFeedback
+from dspy.teleprompt.gepa.gepa import GEPAFeedbackMetric
+
+from eval_protocol.pytest.types import TestFunction
+from eval_protocol.models import EvaluationRow, Message
+
 
 REFLECTION_LM_CONFIGS = {
     "gpt-5": {
@@ -30,3 +38,70 @@ def build_reflection_lm(reflection_lm_name: str) -> LM:
         api_key=reflection_lm_config["api_key"],
         base_url=reflection_lm_config["base_url"],
     )
+
+
+def gold_and_pred_to_row(gold: Example, pred: Prediction) -> EvaluationRow:
+    """
+    Convert a GEPA (gold, pred) pair into an EvaluationRow for an EP `@evaluation_test`.
+
+    Assumptions (aligned with common DSPy usage):
+    - `gold.answer` holds the ground-truth answer.
+    - `pred.answer` holds the model's final answer text.
+    """
+    gt = gold.get("answer", None)
+    ground_truth_str: Optional[str] = str(gt) if gt is not None else None
+
+    content = pred.get("answer", "")
+
+    return EvaluationRow(
+        messages=[
+            Message(role="assistant", content=str(content))
+        ],  # TODO: for some evals, you might need system / user message too.
+        ground_truth=ground_truth_str,
+    )
+
+
+def row_to_prediction(row: EvaluationRow) -> ScoreWithFeedback:
+    """
+    Convert an EvaluationRow into a GEPA-compatible ScoreWithFeedback
+    (implemented as a dspy.Prediction subclass in dspy.teleprompt.gepa).
+    """
+    if row.evaluation_result is None:
+        return dspy.Prediction(
+            score=0.0,
+            feedback="No evaluation_result was produced by the evaluation_test.",
+        )
+
+    score = float(row.evaluation_result.score or 0.0)
+    feedback = row.evaluation_result.reason or f"This trajectory got a score of {score}."
+    return dspy.Prediction(score=score, feedback=feedback)
+
+
+def ep_test_to_gepa_metric(
+    test_fn: TestFunction,
+) -> GEPAFeedbackMetric:
+    """
+    Adapter: convert an EP-style `test_fn(row: EvaluationRow) -> EvaluationRow` into
+    a GEPAFeedbackMetric-compatible callable.
+
+    The resulting metric:
+    - Constructs an EvaluationRow from (gold, pred) using a simple heuristic.
+    - Applies the EP test_fn to populate `row.evaluation_result`.
+    - Returns a dspy.Prediction(score, feedback) derived from that result.
+    """
+
+    def metric(
+        gold: Example,
+        pred: Prediction,
+        trace: Optional[DSPyTrace] = None,
+        pred_name: Optional[str] = None,
+        pred_trace: Optional[DSPyTrace] = None,
+    ) -> ScoreWithFeedback:
+        row = gold_and_pred_to_row(gold, pred)
+
+        evaluated_row: EvaluationRow = test_fn(row)  # pyright: ignore
+        # TODO: this is problematic. for groupwise, we will have to extend this to handle list[EvaluationRow]
+
+        return row_to_prediction(evaluated_row)
+
+    return metric

From 35a3267d7e23832921db61acb25710acaaae8a5a Mon Sep 17 00:00:00 2001
From: Shrey Modi <shreycricket10@gmail.com>
Date: Tue, 9 Dec 2025 11:19:54 -0800
Subject: [PATCH 08/15] gepa wokring

---
 eval_protocol/benchmarks/test_aime25.py |  30 +-
 eval_protocol/training/__init__.py      |  20 +-
 eval_protocol/training/gepa_trainer.py  | 505 ++++++++++++++++++++++--
 eval_protocol/training/gepa_utils.py    | 434 ++++++++++++++++++--
 4 files changed, 925 insertions(+), 64 deletions(-)

diff --git a/eval_protocol/benchmarks/test_aime25.py b/eval_protocol/benchmarks/test_aime25.py
index c921cef7..6994a0ca 100644
--- a/eval_protocol/benchmarks/test_aime25.py
+++ b/eval_protocol/benchmarks/test_aime25.py
@@ -123,15 +123,14 @@ def aime2025_dataset_adapter(rows: List[Dict[str, Any]]) -> List[EvaluationRow]:
     completion_params=[
         {
             "max_tokens": 131000,
-            "extra_body": {"reasoning_effort": "low"},
-            "model": "fireworks_ai/accounts/fireworks/models/gpt-oss-120b",
+            "model": "fireworks_ai/accounts/fireworks/models/deepseek-v3p1-terminus",
         }
     ],
     rollout_processor=SingleTurnRolloutProcessor(),
     aggregation_method="mean",
     passed_threshold=0.8,
     num_runs=8,
-    max_dataset_rows=2,
+    max_dataset_rows=None,  # Use full dataset
     max_concurrent_rollouts=4,
     mode="pointwise",
 )
@@ -182,14 +181,31 @@ def test_aime25_pointwise(row: EvaluationRow) -> EvaluationRow:
 
 
 if __name__ == "__main__":
-    trainer = GEPATrainer(test_aime25_pointwise)
-    reflection_lm = build_reflection_lm("gpt-5")
+    import asyncio
+
+    trainer = GEPATrainer(
+        test_aime25_pointwise,
+        train_ratio=0.5,  # 50% for training (15 problems)
+        val_ratio=0.3,  # 30% for validation (9 problems)
+        # test_ratio = 20% (6 problems) - calculated automatically
+    )
+
+    # Use same Fireworks model for both main and reflection
+    reflection_lm = build_reflection_lm("fireworks_ai/accounts/fireworks/models/deepseek-v3p1-terminus")
 
     optimized_program = trainer.train(
-        num_threads=32,
+        num_threads=4,  # Reduced from 32 to avoid API timeouts
         track_stats=True,
-        reflection_minibatch_size=3,
+        reflection_minibatch_size=5,  # Reduced to limit concurrent requests
         reflection_lm=reflection_lm,
     )
 
+    # Option 1: Quick DSPy evaluation (doesn't use EP infrastructure)
+    print("\n=== DSPy Evaluation ===")
     print(trainer.evaluate(optimized_program))
+
+    # Option 2: Full EP evaluation (uses LLM proxy, Fireworks tracing, etc.)
+    # This goes through the normal @evaluation_test pipeline
+    print("\n=== EP Evaluation (with tracing) ===")
+    results = trainer.run_ep_evaluation(optimized_program)
+    print(f"Final EP Score: {results['score']:.3f}")
diff --git a/eval_protocol/training/__init__.py b/eval_protocol/training/__init__.py
index fcb904c1..122b6a7a 100644
--- a/eval_protocol/training/__init__.py
+++ b/eval_protocol/training/__init__.py
@@ -1,3 +1,19 @@
-from gepa_trainer import GEPATrainer
+from .gepa_trainer import GEPATrainer
+from .gepa_utils import (
+    DSPyModuleType,
+    DSPyModuleFactory,
+    create_single_turn_program,
+    create_signature,
+    build_reflection_lm,
+)
 
-__all__ = ["GEPATrainer"]
+__all__ = [
+    "GEPATrainer",
+    # DSPy module creation utilities
+    "DSPyModuleType",
+    "DSPyModuleFactory",
+    "create_single_turn_program",
+    "create_signature",
+    # Reflection LM helpers
+    "build_reflection_lm",
+]
diff --git a/eval_protocol/training/gepa_trainer.py b/eval_protocol/training/gepa_trainer.py
index 6fc2a08a..08541062 100644
--- a/eval_protocol/training/gepa_trainer.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -1,17 +1,27 @@
-from typing import Any, Dict, Literal
+import asyncio
+from typing import Any, Dict, List, Literal
 
 import dspy
 from dspy.clients.lm import LM
-from dspy.primitives import Module
+from dspy.primitives import Module, Example
 from dspy.teleprompt.gepa.gepa import GEPA
 from gepa.core.adapter import ProposalFn
 from gepa.proposer.reflective_mutation.base import ReflectionComponentSelector
 
-from eval_protocol.models import EPParameters, EvaluationRow
-from eval_protocol.pytest.types import TestFunction
+from eval_protocol.models import EPParameters, EvaluationRow, Message
+from eval_protocol.pytest.types import TestFunction, RolloutProcessorConfig
 from eval_protocol.training.trainer import Trainer
 from eval_protocol.training.utils import build_ep_parameters_from_test
-from eval_protocol.training.gepa_utils import ep_test_to_gepa_metric
+from eval_protocol.training.gepa_utils import (
+    ep_test_to_gepa_metric,
+    create_single_turn_program,
+    configure_dspy_lm,
+    extract_system_prompt_from_rows,
+    evaluation_rows_to_dspy_examples,
+    train_val_test_split,
+    DSPyModuleType,
+    DSPyModuleFactory,
+)
 
 
 class GEPATrainer(Trainer):
@@ -19,34 +29,207 @@ class GEPATrainer(Trainer):
     High-level entrypoint for running GEPA-style training against an existing
     `@evaluation_test`-decorated function.
 
-    This class is intentionally minimal for now:
-    - It captures `EPParameters` from the provided test function via
-      `build_ep_parameters_from_test`.
-    - It stores any GEPA-related configuration kwargs for future use.
-    - The actual GEPA optimization loop is left as a TODO.
+    This trainer:
+    1. Extracts configuration from the @evaluation_test decorator
+    2. Creates a DSPy ChainOfThought program (mirrors SingleTurnRolloutProcessor)
+    3. Converts the EP dataset to DSPy format
+    4. Uses EP's test function as the GEPA metric
+    5. Runs GEPA optimization to find the best system prompt
+
+    The optimized system prompt can then be used with EP's rollout processor
+    for final evaluation.
     """
 
-    def __init__(self, test_fn: TestFunction) -> None:
+    def __init__(
+        self,
+        test_fn: TestFunction,
+        *,
+        # Dataset splitting
+        train_ratio: float = 0.8,
+        val_ratio: float = 0.1,
+        seed: int = 42,
+        # DSPy signature configuration
+        input_field: str = "problem",
+        output_field: str = "answer",
+        input_desc: str | None = None,
+        output_desc: str | None = None,
+        # DSPy module configuration
+        module_type: DSPyModuleType | str = DSPyModuleType.CHAIN_OF_THOUGHT,
+        module_factory: DSPyModuleFactory | None = None,
+        # Custom program (overrides automatic creation)
+        program: Module | None = None,
+    ) -> None:
         """
         Args:
             test_fn: The `@evaluation_test`-decorated function defining the eval.
+            train_ratio: Proportion of data for training (default 0.8)
+            val_ratio: Proportion of data for validation (default 0.1)
+            seed: Random seed for dataset splitting
+            input_field: Name of the input field in DSPy signature (default: "problem")
+            output_field: Name of the output field in DSPy signature (default: "answer")
+            input_desc: Optional description for the input field
+            output_desc: Optional description for the output field
+            module_type: Which DSPy module to use:
+                - PREDICT: Simple input → output
+                - CHAIN_OF_THOUGHT: Adds reasoning (default, good for complex tasks)
+                - PROGRAM_OF_THOUGHT: Generates code to solve problems
+            module_factory: Custom factory to create DSPy module. Overrides module_type.
+            program: Pre-built DSPy module. If provided, skips automatic creation.
+
+        Examples:
+            # Default: ChainOfThought for math
+            trainer = GEPATrainer(test_fn)
+
+            # Simple classification
+            trainer = GEPATrainer(
+                test_fn,
+                input_field="text",
+                output_field="label",
+                module_type=DSPyModuleType.PREDICT,
+            )
+
+            # Custom DSPy module
+            my_program = dspy.ChainOfThought(MySignature)
+            trainer = GEPATrainer(test_fn, program=my_program)
         """
         super().__init__(test_fn)
         self.ep_params: EPParameters = build_ep_parameters_from_test(test_fn)
 
+        # Store configuration
+        self._input_field = input_field
+        self._output_field = output_field
+
+        # Configure DSPy to use the same LLM as EP
+        configure_dspy_lm(self.ep_params)
+
+        # Wrap the EP test function as a GEPA metric
         self.metric = ep_test_to_gepa_metric(test_fn)
 
-        self.program = ...  # TODO @shreymodi1: converting between a program (dspy.Module) and rollout processors is a bit tricky. maybe start with single turn
+        # Load and split the dataset
+        self._rows: List[EvaluationRow] = self._load_dataset()
+        train_rows, val_rows, test_rows = train_val_test_split(
+            self._rows,
+            train_ratio=train_ratio,
+            val_ratio=val_ratio,
+            seed=seed,
+        )
+
+        # Extract the system prompt from the dataset (this is what GEPA will optimize!)
+        self._initial_system_prompt = extract_system_prompt_from_rows(self._rows)
+
+        # Debug: Print initial setup info
+        print("\n" + "=" * 80)
+        print("GEPA TRAINER INITIALIZATION")
+        print("=" * 80)
+        print(f"\n📊 Dataset loaded: {len(self._rows)} total rows")
+        print(f"   - Train: {len(train_rows)} rows")
+        print(f"   - Val: {len(val_rows)} rows")
+        print(f"   - Test: {len(test_rows)} rows")
+        print("\n📝 Initial System Prompt (what GEPA will optimize):")
+        print("-" * 40)
+        print(
+            self._initial_system_prompt[:500] + "..."
+            if self._initial_system_prompt and len(self._initial_system_prompt) > 500
+            else self._initial_system_prompt
+        )
+        print("-" * 40)
+
+        # Create or use provided DSPy program
+        if program is not None:
+            # Use the provided program directly
+            self.program: Module = program
+        else:
+            # Create DSPy program (mirrors SingleTurnRolloutProcessor)
+            # - system_prompt → signature.instructions (GEPA optimizes this!)
+            # - user message → input field
+            # - assistant response → output field
+            self.program = create_single_turn_program(
+                system_prompt=self._initial_system_prompt,
+                input_field=input_field,
+                output_field=output_field,
+                module_type=module_type,
+                input_desc=input_desc,
+                output_desc=output_desc,
+                module_factory=module_factory,
+            )
+
+        # Convert EP rows to DSPy Examples
+        self.train_set: List[Example] = evaluation_rows_to_dspy_examples(train_rows, input_field, output_field)
+        self.val_set: List[Example] = evaluation_rows_to_dspy_examples(val_rows, input_field, output_field)
+        self.test_set: List[Example] = evaluation_rows_to_dspy_examples(test_rows, input_field, output_field)
+
+        # Debug: Print example info
+        print("\n📦 DSPy Examples created:")
+        print(f"   Input field: '{input_field}', Output field: '{output_field}'")
+        if self.train_set:
+            ex = self.train_set[0]
+            print("\n   Sample train example:")
+            print(f"   - {input_field}: {str(getattr(ex, input_field, ''))[:200]}...")
+            print(f"   - {output_field}: {str(getattr(ex, output_field, ''))}")
+        print("=" * 80 + "\n")
+
+    def _load_dataset(self) -> List[EvaluationRow]:
+        """
+        Load the dataset from ep_params.
+
+        Supports:
+        - input_rows: Pre-constructed EvaluationRow objects
+        - input_dataset: Paths to JSONL files (requires dataset_adapter)
+        - input_messages: Raw message lists
+        """
+        ep = self.ep_params
+
+        # Case 1: Pre-constructed rows
+        if ep.input_rows:
+            return list(ep.input_rows)
+
+        # Case 2: Dataset paths with adapter
+        if ep.input_dataset and ep.dataset_adapter:
+            from eval_protocol.common_utils import load_jsonl
+
+            all_data: List[Dict[str, Any]] = []
+            dataset_paths = ep.input_dataset if isinstance(ep.input_dataset, list) else [ep.input_dataset]
+
+            for path in dataset_paths:
+                all_data.extend(load_jsonl(path))
+
+            # Apply max_dataset_rows limit
+            if ep.max_dataset_rows:
+                all_data = all_data[: ep.max_dataset_rows]
 
-        self.train_set, self.val_set, self.test_set = (
-            ...,
-            ...,
-            ...,
-        )  # TODO @shreymodi1. need to convert our input_dataset to a train set
+            return ep.dataset_adapter(all_data)
+
+        # Case 3: Input messages (convert to rows)
+        if ep.input_messages:
+            from eval_protocol.models import Message
+
+            rows = []
+            for messages in ep.input_messages:
+                rows.append(EvaluationRow(messages=messages))
+            return rows
+
+        raise ValueError(
+            "No dataset found in ep_params. "
+            "Provide input_rows, input_dataset (with dataset_adapter), or input_messages."
+        )
+
+    @property
+    def initial_system_prompt(self) -> str | None:
+        """The original system prompt extracted from the dataset."""
+        return self._initial_system_prompt
+
+    def get_optimized_system_prompt(self, optimized_program: Module) -> str:
+        """
+        Extract the optimized system prompt from a GEPA-optimized program.
+
+        This can be used with EP's rollout processor via system_prompt_override.
+        """
+        # GEPA stores optimized instructions in the signature
+        return optimized_program.predict.signature.instructions
 
     def train(
         self,
-        auto: Literal["light", "medium", "heavy"] | None = None,
+        auto: Literal["light", "medium", "heavy"] | None = "light",
         max_full_evals: int | None = None,
         max_metric_calls: int | None = None,
         reflection_minibatch_size: int = 3,
@@ -68,7 +251,6 @@ def train(
         wandb_init_kwargs: dict[str, Any] | None = None,
         track_best_outputs: bool = False,
         warn_on_score_mismatch: bool = True,
-        enable_tool_optimization: bool = False,
         use_mlflow: bool = False,
         seed: int | None = 0,
         gepa_kwargs: dict | None = None,
@@ -99,12 +281,44 @@ def train(
             "wandb_init_kwargs": wandb_init_kwargs,
             "track_best_outputs": track_best_outputs,
             "warn_on_score_mismatch": warn_on_score_mismatch,
-            "enable_tool_optimization": enable_tool_optimization,
             "use_mlflow": use_mlflow,
             "seed": seed,
         }
         gepa_args.update(gepa_kwargs or {})
 
+        print("\n" + "=" * 80)
+        print("GEPA TRAINING STARTED")
+        print("=" * 80)
+        print(f"📋 Program type: {type(self.program).__name__}")
+
+        # Get signature - ChainOfThought stores it in .predict.signature
+        sig = None
+        if hasattr(self.program, "signature"):
+            sig = self.program.signature
+        elif hasattr(self.program, "predict") and hasattr(self.program.predict, "signature"):
+            sig = self.program.predict.signature
+
+        if sig:
+            print(f"📋 Signature: {sig}")
+            print("📋 Initial Instructions:")
+            print("-" * 40)
+            print(sig.instructions if sig.instructions else "None")
+            print("-" * 40)
+        else:
+            print("📋 Signature: N/A")
+
+        print(f"📋 Train set size: {len(self.train_set)}")
+        print(f"📋 Val set size: {len(self.val_set)}")
+        print(f"📋 Test set size: {len(self.test_set)}")
+        print(f"📋 GEPA auto mode: {gepa_args.get('auto', 'N/A')}")
+        print(f"📋 Reflection minibatch size: {gepa_args.get('reflection_minibatch_size', 3)}")
+        print("=" * 80 + "\n")
+
+        # Enable verbose logging from DSPy/GEPA
+        import logging
+
+        logging.getLogger("dspy.teleprompt.gepa.gepa").setLevel(logging.INFO)
+
         optimizer = GEPA(
             metric=self.metric,
             **gepa_args,
@@ -116,22 +330,247 @@ def train(
             valset=self.val_set,
         )
 
+        print("\n" + "=" * 80)
+        print("GEPA TRAINING COMPLETE")
+        print("=" * 80)
+
+        # Print detailed results if track_stats was enabled
+        if hasattr(optimized_program, "detailed_results"):
+            results = optimized_program.detailed_results
+            print("\n📊 OPTIMIZATION STATS:")
+            print(f"   Total metric calls: {results.total_metric_calls}")
+            print(f"   Full val evals: {results.num_full_val_evals}")
+            print(f"   Best candidate index: {results.best_idx}")
+            print(f"   Best val score: {results.val_aggregate_scores[results.best_idx]:.3f}")
+
+            print("\n📈 ALL CANDIDATE SCORES:")
+            for i, score in enumerate(results.val_aggregate_scores):
+                marker = " 🏆" if i == results.best_idx else ""
+                print(f"   Candidate {i}: {score:.3f}{marker}")
+
+        optimized_instructions = self.get_optimized_system_prompt(optimized_program)
+        print("\n🎯 OPTIMIZED SYSTEM PROMPT:")
+        print("-" * 60)
+        print(optimized_instructions)
+        print("-" * 60)
+
+        # Compare with initial
+        print("\n📝 COMPARISON:")
+        print(f"   Initial prompt length: {len(self._initial_system_prompt or '')} chars")
+        print(f"   Optimized prompt length: {len(optimized_instructions)} chars")
+        if self._initial_system_prompt != optimized_instructions:
+            print("   ✅ Prompt was CHANGED by GEPA")
+        else:
+            print("   ⚠️  Prompt was NOT changed (model may already be optimal or no failures to learn from)")
+
+        print("=" * 80 + "\n")
+
         return optimized_program
 
-    def evaluate(self, optimized_program: Module) -> list[EvaluationRow]:
-        # convert back to EP
+    def evaluate(
+        self,
+        optimized_program: Module,
+        num_threads: int = 32,
+        display_table: bool = True,
+        display_progress: bool = True,
+    ) -> dspy.evaluate.EvaluationResult:
+        """
+        Evaluate the optimized program on the test set using DSPy's Evaluate.
 
-        # and then just run our evaluation_test function on the optimized program.
+        Args:
+            optimized_program: The GEPA-optimized program
+            num_threads: Number of parallel threads for evaluation
+            display_table: Whether to display results table
+            display_progress: Whether to show progress bar
+
+        Returns:
+            DSPy EvaluationResult with score and per-example results
+        """
+        evaluator = dspy.Evaluate(
+            devset=self.test_set,
+            metric=self.metric,
+            num_threads=num_threads,
+            display_table=display_table,
+            display_progress=display_progress,
+        )
+
+        return evaluator(optimized_program)
 
-        # OR we can evaluate using dspy.Evaluate
+    def evaluate_baseline(
+        self,
+        num_threads: int = 32,
+        display_table: bool = True,
+        display_progress: bool = True,
+    ) -> dspy.evaluate.EvaluationResult:
+        """
+        Evaluate the unoptimized baseline program on the test set.
+
+        Useful for comparing before/after GEPA optimization.
+        """
+        return self.evaluate(
+            self.program,
+            num_threads=num_threads,
+            display_table=display_table,
+            display_progress=display_progress,
+        )
+
+    def _inject_system_prompt(self, rows: List[EvaluationRow], new_system_prompt: str) -> List[EvaluationRow]:
+        """
+        Create copies of rows with the system prompt replaced.
+        """
+        modified_rows = []
+        for row in rows:
+            new_row = row.model_copy(deep=True)
+            new_messages = []
+            system_found = False
+            for msg in new_row.messages:
+                if msg.role == "system" and not system_found:
+                    # Replace the first system message
+                    new_messages.append(Message(role="system", content=new_system_prompt))
+                    system_found = True
+                else:
+                    new_messages.append(msg)
+            # If no system message found, prepend one
+            if not system_found:
+                new_messages.insert(0, Message(role="system", content=new_system_prompt))
+            new_row.messages = new_messages
+            modified_rows.append(new_row)
+        return modified_rows
+
+    async def evaluate_with_ep(
+        self,
+        optimized_program: Module,
+        *,
+        use_test_set: bool = True,
+        max_concurrent_rollouts: int = 8,
+    ) -> Dict[str, Any]:
+        """
+        Run final evaluation through the normal EP infrastructure.
+
+        This uses the same LLM proxy (EP_LLM_API_BASE) and tracing as a normal
+        @evaluation_test job.
+
+        Args:
+            optimized_program: The GEPA-optimized program
+            use_test_set: If True, evaluate on test set. If False, use full dataset.
+            max_concurrent_rollouts: Maximum concurrent LLM calls
+
+        Returns:
+            Dict with evaluation results:
+            - 'rows': List of evaluated EvaluationRow objects
+            - 'score': Aggregate score
+            - 'optimized_prompt': The prompt used for evaluation
+        """
+        from eval_protocol.pytest.default_single_turn_rollout_process import SingleTurnRolloutProcessor
+        from eval_protocol.pytest.execution import execute_pytest
+        from eval_protocol.logging import default_logger
 
-        # evaluate = dspy.Evaluate(
-        #     devset=self.test_set,
-        #     metric=self.metric,
-        #     num_threads=32,
-        #     display_table=True,
-        #     display_progress=True
-        # )
+        # Get optimized system prompt
+        optimized_prompt = self.get_optimized_system_prompt(optimized_program)
 
-        # return evaluate(self.optimized_program)
-        ...
+        print("\n" + "=" * 80)
+        print("RUNNING EP EVALUATION (with LLM proxy & tracing)")
+        print("=" * 80)
+        print(f"📋 Using optimized prompt ({len(optimized_prompt)} chars)")
+
+        # Get rows to evaluate
+        if use_test_set:
+            # Reconstruct test rows from test_set examples
+            _, _, test_rows = train_val_test_split(
+                self._rows,
+                train_ratio=0.5,  # Match the ratio used in training
+                val_ratio=0.3,
+                seed=42,
+            )
+            rows_to_eval = test_rows
+            print(f"📊 Evaluating on TEST SET: {len(rows_to_eval)} rows")
+        else:
+            rows_to_eval = self._rows
+            print(f"📊 Evaluating on FULL DATASET: {len(rows_to_eval)} rows")
+
+        # Inject optimized system prompt into rows
+        modified_rows = self._inject_system_prompt(rows_to_eval, optimized_prompt)
+
+        # Set up rollout processor config
+        completion_params = self.ep_params.completion_params
+        if isinstance(completion_params, list):
+            completion_params = completion_params[0] if completion_params else {}
+        completion_params = completion_params or {}
+
+        # Create semaphore for concurrency control
+        semaphore = asyncio.Semaphore(max_concurrent_rollouts)
+
+        config = RolloutProcessorConfig(
+            completion_params=completion_params,
+            mcp_config_path="",
+            server_script_path=None,
+            steps=30,
+            logger=default_logger,
+            semaphore=semaphore,
+            kwargs={},
+            exception_handler_config=None,
+        )
+
+        # Run rollouts through EP infrastructure (uses EP_LLM_API_BASE)
+        rollout_processor = SingleTurnRolloutProcessor()
+        rollout_processor.setup()
+
+        print("🚀 Running rollouts through EP infrastructure...")
+        print(f"   Model: {completion_params.get('model', 'N/A')}")
+
+        try:
+            # Execute rollouts
+            tasks = rollout_processor(modified_rows, config)
+            rolled_out_rows = await asyncio.gather(*tasks)
+
+            print(f"✅ Rollouts complete: {len(rolled_out_rows)} rows")
+
+            # Run evaluation function on each row
+            evaluated_rows = []
+            scores = []
+
+            for row in rolled_out_rows:
+                # Call the original test function for evaluation
+                evaluated_row = await execute_pytest(
+                    self.test_fn,
+                    processed_row=row,  # pyright: ignore[reportArgumentType]
+                )
+                evaluated_rows.append(evaluated_row)
+
+                # Extract score - evaluated_row is EvaluationRow from execute_pytest
+                if hasattr(evaluated_row, "evaluation_result") and evaluated_row.evaluation_result:  # pyright: ignore[reportAttributeAccessIssue]
+                    scores.append(evaluated_row.evaluation_result.score)  # pyright: ignore[reportAttributeAccessIssue]
+
+            # Calculate aggregate score
+            avg_score = sum(scores) / len(scores) if scores else 0.0
+
+            print("\n📊 EVALUATION RESULTS:")
+            print(f"   Total rows: {len(evaluated_rows)}")
+            print(f"   Aggregate score: {avg_score:.3f}")
+            print(f"   Passing: {sum(1 for s in scores if s >= 0.5)}/{len(scores)}")
+            print("=" * 80 + "\n")
+
+            return {
+                "rows": evaluated_rows,
+                "score": avg_score,
+                "scores": scores,
+                "optimized_prompt": optimized_prompt,
+            }
+
+        finally:
+            rollout_processor.cleanup()
+
+    def run_ep_evaluation(
+        self,
+        optimized_program: Module,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        """
+        Synchronous wrapper for evaluate_with_ep.
+
+        Example:
+            trainer = GEPATrainer(test_fn)
+            optimized = trainer.train()
+            results = trainer.run_ep_evaluation(optimized)
+        """
+        return asyncio.run(self.evaluate_with_ep(optimized_program, **kwargs))
diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
index ec50245c..7a3b60c3 100644
--- a/eval_protocol/training/gepa_utils.py
+++ b/eval_protocol/training/gepa_utils.py
@@ -1,5 +1,5 @@
 import os
-from typing import Optional
+from typing import Any, Optional, Tuple
 
 import dspy
 from dspy.clients.lm import LM
@@ -8,36 +8,88 @@
 from dspy.teleprompt.gepa.gepa import GEPAFeedbackMetric
 
 from eval_protocol.pytest.types import TestFunction
-from eval_protocol.models import EvaluationRow, Message
+from eval_protocol.models import EvaluationRow, EPParameters, Message
 
 
+# =============================================================================
+# Reflection LM configurations for GEPA
+# =============================================================================
+
+# Reflection LM configs use LiteLLM format: "provider/model_name"
+# API keys should be set via environment variables:
+#   - OPENAI_API_KEY for OpenAI models
+#   - FIREWORKS_API_KEY for Fireworks models
+#   - ANTHROPIC_API_KEY for Anthropic models
+
 REFLECTION_LM_CONFIGS = {
+    # OpenAI models
     "gpt-5": {
-        "model": "gpt-5",
+        "model": "openai/gpt-5",
         "temperature": 1.0,
         "max_tokens": 32000,
-        "api_key": os.getenv("OPENAI_API_KEY"),
-        "base_url": "https://api.openai.com/v1",
     },
-    "kimi-k2-instruct-0905": {
-        "model": "accounts/fireworks/models/kimi-k2-instruct-0905",
-        "temperature": 0.6,  # Kimi recommended temperature
+    "gpt-4o": {
+        "model": "openai/gpt-4o",
+        "temperature": 1.0,
+        "max_tokens": 16000,
+    },
+    # Anthropic models
+    "claude-sonnet": {
+        "model": "anthropic/claude-sonnet-4-20250514",
+        "temperature": 1.0,
+        "max_tokens": 16000,
+    },
+    # Fireworks models
+    "kimi-k2": {
+        "model": "fireworks_ai/accounts/fireworks/models/kimi-k2-instruct-0905",
+        "temperature": 0.6,
         "max_tokens": 131000,
-        "api_key": os.getenv("FIREWORKS_API_KEY"),
-        "base_url": "https://api.fireworks.ai/inference/v1",
+    },
+    "llama-4-maverick": {
+        "model": "fireworks_ai/accounts/fireworks/models/llama4-maverick-instruct-basic",
+        "temperature": 1.0,
+        "max_tokens": 65536,
+    },
+    "deepseek-r1": {
+        "model": "fireworks_ai/accounts/fireworks/models/deepseek-r1",
+        "temperature": 1.0,
+        "max_tokens": 65536,
+    },
+    "qwen3-235b": {
+        "model": "fireworks_ai/accounts/fireworks/models/qwen3-235b-a22b",
+        "temperature": 1.0,
+        "max_tokens": 65536,
     },
 }
 
 
 def build_reflection_lm(reflection_lm_name: str) -> LM:
-    reflection_lm_config = REFLECTION_LM_CONFIGS[reflection_lm_name]
-    return dspy.LM(
-        model=reflection_lm_config["model"],
-        temperature=reflection_lm_config["temperature"],
-        max_tokens=reflection_lm_config["max_tokens"],
-        api_key=reflection_lm_config["api_key"],
-        base_url=reflection_lm_config["base_url"],
-    )
+    """
+    Build a DSPy LM for GEPA's reflection step.
+
+    Args:
+        reflection_lm_name: One of the predefined configs ("gpt-5", "gpt-4o",
+                           "claude-sonnet", "kimi-k2-instruct-0905")
+                           OR a raw LiteLLM model string (e.g., "openai/gpt-4o")
+
+    Returns:
+        A dspy.LM configured for reflection.
+
+    Note: API keys must be set via environment variables:
+        - OPENAI_API_KEY for OpenAI models
+        - FIREWORKS_API_KEY for Fireworks models
+        - ANTHROPIC_API_KEY for Anthropic models
+    """
+    if reflection_lm_name in REFLECTION_LM_CONFIGS:
+        config = REFLECTION_LM_CONFIGS[reflection_lm_name]
+        return dspy.LM(
+            model=config["model"],
+            temperature=config.get("temperature"),
+            max_tokens=config.get("max_tokens"),
+        )
+    else:
+        # Assume it's a raw LiteLLM model string
+        return dspy.LM(model=reflection_lm_name)
 
 
 def gold_and_pred_to_row(gold: Example, pred: Prediction) -> EvaluationRow:
@@ -47,9 +99,14 @@ def gold_and_pred_to_row(gold: Example, pred: Prediction) -> EvaluationRow:
     Assumptions (aligned with common DSPy usage):
     - `gold.answer` holds the ground-truth answer.
     - `pred.answer` holds the model's final answer text.
+
+    Note: ground_truth is preserved in its original type (list, dict, str, etc.)
+    to support structured comparisons like SQL result matching.
     """
     gt = gold.get("answer", None)
-    ground_truth_str: Optional[str] = str(gt) if gt is not None else None
+    # Preserve original type - don't convert to string!
+    # This is important for SQL evaluators that expect list[dict] results
+    ground_truth = gt
 
     content = pred.get("answer", "")
 
@@ -57,7 +114,7 @@ def gold_and_pred_to_row(gold: Example, pred: Prediction) -> EvaluationRow:
         messages=[
             Message(role="assistant", content=str(content))
         ],  # TODO: for some evals, you might need system / user message too.
-        ground_truth=ground_truth_str,
+        ground_truth=ground_truth,
     )
 
 
@@ -88,7 +145,17 @@ def ep_test_to_gepa_metric(
     - Constructs an EvaluationRow from (gold, pred) using a simple heuristic.
     - Applies the EP test_fn to populate `row.evaluation_result`.
     - Returns a dspy.Prediction(score, feedback) derived from that result.
+
+    Note: The @evaluation_test decorator wraps functions as async, so we need to
+    handle both sync and async test functions.
     """
+    import asyncio
+    import inspect
+
+    # Counter for debugging
+    call_count = [0]
+    DEBUG_METRIC = True  # Set to False to disable metric debug output
+    DEBUG_VERBOSE = True  # Set to True to print ALL calls (can be very verbose!)
 
     def metric(
         gold: Example,
@@ -97,11 +164,334 @@ def metric(
         pred_name: Optional[str] = None,
         pred_trace: Optional[DSPyTrace] = None,
     ) -> ScoreWithFeedback:
+        call_count[0] += 1
+
+        should_print = DEBUG_METRIC and (DEBUG_VERBOSE or call_count[0] <= 3)
+
+        if should_print:
+            print(f"\n🔍 METRIC CALL #{call_count[0]}")
+            print("-" * 40)
+            print(f"   Gold (expected): {gold.get('answer', 'N/A')}")
+            print(f"   Pred (model):    {str(pred.get('answer', 'N/A'))[:200]}")
+            if hasattr(pred, "reasoning") and pred.reasoning:
+                print(f"   Reasoning:       {str(pred.reasoning)[:300]}...")
+
         row = gold_and_pred_to_row(gold, pred)
 
-        evaluated_row: EvaluationRow = test_fn(row)  # pyright: ignore
+        # Call the test function - handle both sync and async
+        result = test_fn(row)  # pyright: ignore
+
+        # If it's a coroutine, run it synchronously
+        if inspect.iscoroutine(result):
+            try:
+                loop = asyncio.get_running_loop()
+            except RuntimeError:
+                loop = None
+
+            if loop is not None:
+                # Already in an async context - create a new loop in a thread
+                import concurrent.futures
+
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    future = executor.submit(asyncio.run, result)
+                    evaluated_row: EvaluationRow = future.result()
+            else:
+                # No running loop - safe to use asyncio.run
+                evaluated_row = asyncio.run(result)
+        else:
+            evaluated_row = result  # type: ignore[reportAssignmentType]
+
         # TODO: this is problematic. for groupwise, we will have to extend this to handle list[EvaluationRow]
 
-        return row_to_prediction(evaluated_row)
+        score_result = row_to_prediction(evaluated_row)
+
+        if should_print:
+            print(f"   Score:           {score_result.score}")
+            print(f"   Feedback:        {str(score_result.feedback)[:200]}")
+            print("-" * 40)
+
+        return score_result
 
     return metric
+
+
+# =============================================================================
+# DSPy Program Creation (maps SingleTurnRolloutProcessor → DSPy Module)
+# =============================================================================
+
+from typing import Callable, Type
+from enum import Enum
+
+
+class DSPyModuleType(Enum):
+    """Available DSPy module types for single-turn rollouts."""
+
+    PREDICT = "predict"  # Simple input → output
+    CHAIN_OF_THOUGHT = "chain_of_thought"  # Adds reasoning before output (good for math)
+    PROGRAM_OF_THOUGHT = "program_of_thought"  # Generates code to solve problems
+
+
+# Type alias for custom module factory
+DSPyModuleFactory = Callable[[dspy.Signature], dspy.Module]
+
+
+def create_signature(
+    input_field: str = "problem",
+    output_field: str = "answer",
+    instructions: str | None = None,
+    input_desc: str | None = None,
+    output_desc: str | None = None,
+) -> dspy.Signature:
+    """
+    Create a DSPy Signature for single-turn tasks.
+
+    Args:
+        input_field: Name of the input field (default: "problem")
+        output_field: Name of the output field (default: "answer")
+        instructions: System prompt / instructions (what GEPA optimizes!)
+        input_desc: Description for the input field
+        output_desc: Description for the output field
+
+    Returns:
+        A dspy.Signature configured for the task.
+    """
+    # Build signature string
+    signature_str = f"{input_field} -> {output_field}"
+
+    # Create base signature
+    if instructions:
+        sig = dspy.Signature(signature_str, instructions=instructions)
+    else:
+        sig = dspy.Signature(signature_str)
+
+    # Add field descriptions if provided
+    if input_desc:
+        sig = sig.with_updated_fields(input_field, desc=input_desc)
+    if output_desc:
+        sig = sig.with_updated_fields(output_field, desc=output_desc)
+
+    return sig
+
+
+def create_single_turn_program(
+    system_prompt: str | None = None,
+    input_field: str = "problem",
+    output_field: str = "answer",
+    module_type: DSPyModuleType | str = DSPyModuleType.CHAIN_OF_THOUGHT,
+    input_desc: str | None = None,
+    output_desc: str | None = None,
+    module_factory: DSPyModuleFactory | None = None,
+) -> dspy.Module:
+    """
+    Create a DSPy program that mirrors SingleTurnRolloutProcessor.
+
+    This is the general mapping:
+    - SingleTurnRolloutProcessor: system message + user message → LLM → assistant response
+    - DSPy Module: instructions + input field → LLM → output field
+
+    GEPA optimizes the `instructions` (system prompt equivalent)!
+
+    Args:
+        system_prompt: The system prompt (becomes signature.instructions).
+        input_field: Name of the input field (default: "problem")
+        output_field: Name of the output field (default: "answer")
+        module_type: Which DSPy module to use:
+            - PREDICT: Simple input → output
+            - CHAIN_OF_THOUGHT: Adds reasoning before output (default, good for complex tasks)
+            - PROGRAM_OF_THOUGHT: Generates code to solve problems
+        input_desc: Optional description for the input field
+        output_desc: Optional description for the output field
+        module_factory: Custom factory function to create the module.
+                       If provided, overrides module_type.
+                       Signature: (dspy.Signature) -> dspy.Module
+
+    Returns:
+        A DSPy module ready for GEPA optimization.
+
+    Examples:
+        # Default: ChainOfThought for math
+        program = create_single_turn_program(system_prompt="Solve step by step")
+
+        # Simple classification
+        program = create_single_turn_program(
+            input_field="text",
+            output_field="label",
+            module_type=DSPyModuleType.PREDICT
+        )
+
+        # Custom module
+        program = create_single_turn_program(
+            system_prompt="...",
+            module_factory=lambda sig: MyCustomModule(sig)
+        )
+    """
+    # Create the signature
+    sig = create_signature(
+        input_field=input_field,
+        output_field=output_field,
+        instructions=system_prompt,
+        input_desc=input_desc,
+        output_desc=output_desc,
+    )
+
+    # Use custom factory if provided
+    if module_factory is not None:
+        return module_factory(sig)
+
+    # Convert string to enum if needed
+    if isinstance(module_type, str):
+        module_type = DSPyModuleType(module_type)
+
+    # Create the appropriate module type
+    if module_type == DSPyModuleType.PREDICT:
+        return dspy.Predict(sig)
+    elif module_type == DSPyModuleType.CHAIN_OF_THOUGHT:
+        return dspy.ChainOfThought(sig)
+    elif module_type == DSPyModuleType.PROGRAM_OF_THOUGHT:
+        return dspy.ProgramOfThought(sig)
+    else:
+        raise ValueError(f"Unknown module type: {module_type}")
+
+
+def configure_dspy_lm(ep_params: EPParameters) -> None:
+    """
+    Configure DSPy to use the same LLM as the EP evaluation.
+
+    Extracts model info from ep_params.completion_params and configures dspy.
+
+    DSPy uses LiteLLM under the hood, so:
+    - Model format: "provider/model_name" (e.g., "openai/gpt-4o", "fireworks_ai/...")
+    - API keys: Set via environment variables (OPENAI_API_KEY, FIREWORKS_API_KEY, etc.)
+    """
+    raw_params = ep_params.completion_params
+
+    # Handle completion_params being a list (for sweeps) - use the first one
+    if isinstance(raw_params, list):
+        completion_params = raw_params[0] if raw_params else {}
+    else:
+        completion_params = raw_params or {}
+
+    # Extract model name (expected to already be in LiteLLM format)
+    model = completion_params.get("model", "openai/gpt-4")
+
+    # Extract optional parameters
+    temperature = completion_params.get("temperature")  # None = use provider default
+    max_tokens = completion_params.get("max_tokens")  # None = use provider default
+
+    # Build kwargs - only include non-None values
+    lm_kwargs: dict[str, Any] = {"model": model}
+    if temperature is not None:
+        lm_kwargs["temperature"] = temperature
+    if max_tokens is not None:
+        lm_kwargs["max_tokens"] = max_tokens
+
+    # Pass through any extra kwargs from completion_params that DSPy/LiteLLM supports
+    passthrough_keys = ["num_retries", "cache"]
+    for key in passthrough_keys:
+        if key in completion_params:
+            lm_kwargs[key] = completion_params[key]
+
+    lm = dspy.LM(**lm_kwargs)
+    dspy.configure(lm=lm)
+
+
+# =============================================================================
+# Dataset Conversion (EvaluationRow → DSPy Example)
+# =============================================================================
+
+
+def extract_system_prompt_from_rows(rows: list[EvaluationRow]) -> str | None:
+    """
+    Extract the system prompt from a list of EvaluationRows.
+
+    Assumes all rows have the same system prompt (common in benchmarks).
+    Returns the first system message content found, or None.
+    """
+    for row in rows:
+        system_msg = row.get_system_message()
+        if system_msg and system_msg.content:
+            content = system_msg.content
+            return str(content) if content else None
+    return None
+
+
+def extract_user_content(row: EvaluationRow) -> str:
+    """Extract the user message content from an EvaluationRow."""
+    user_msg = row.get_first_user_message()
+    if user_msg and user_msg.content:
+        return str(user_msg.content)
+    return ""
+
+
+def evaluation_row_to_dspy_example(
+    row: EvaluationRow,
+    input_field: str = "problem",
+    output_field: str = "answer",
+) -> Example:
+    """
+    Convert an EvaluationRow to a DSPy Example.
+
+    Maps:
+    - User message content → input_field (e.g., "problem")
+    - ground_truth → output_field (e.g., "answer")
+
+    Note: ground_truth is preserved in its original type to support
+    structured comparisons (e.g., SQL result matching with list[dict]).
+    """
+    # Extract user message as input
+    input_content = extract_user_content(row)
+
+    # Ground truth is the expected output - preserve original type!
+    # Don't convert to string - this breaks SQL evaluators that expect list[dict]
+    output_content = row.ground_truth if row.ground_truth is not None else ""
+
+    return dspy.Example(
+        **{
+            input_field: input_content,
+            output_field: output_content,
+        }
+    ).with_inputs(input_field)
+
+
+def evaluation_rows_to_dspy_examples(
+    rows: list[EvaluationRow],
+    input_field: str = "problem",
+    output_field: str = "answer",
+) -> list[Example]:
+    """Convert a list of EvaluationRows to DSPy Examples."""
+    return [evaluation_row_to_dspy_example(row, input_field, output_field) for row in rows]
+
+
+def train_val_test_split(
+    rows: list[EvaluationRow],
+    train_ratio: float = 0.8,
+    val_ratio: float = 0.1,
+    seed: int = 42,
+) -> Tuple[list[EvaluationRow], list[EvaluationRow], list[EvaluationRow]]:
+    """
+    Split EvaluationRows into train/val/test sets.
+
+    Args:
+        rows: List of EvaluationRow objects
+        train_ratio: Proportion for training (default 0.8)
+        val_ratio: Proportion for validation (default 0.1)
+        seed: Random seed for reproducibility
+
+    Returns:
+        Tuple of (train_rows, val_rows, test_rows)
+    """
+    import random
+
+    # Copy and shuffle
+    shuffled = list(rows)
+    random.Random(seed).shuffle(shuffled)
+
+    n = len(shuffled)
+    train_end = int(n * train_ratio)
+    val_end = int(n * (train_ratio + val_ratio))
+
+    train_rows = shuffled[:train_end]
+    val_rows = shuffled[train_end:val_end]
+    test_rows = shuffled[val_end:]
+
+    return train_rows, val_rows, test_rows

From 2d787bfc69931bfa82a3d40d8089a909aa69748a Mon Sep 17 00:00:00 2001
From: Shrey Modi <shreycricket10@gmail.com>
Date: Tue, 9 Dec 2025 11:20:25 -0800
Subject: [PATCH 09/15] gepa work

---
 eval_protocol/training/gepa_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
index 7a3b60c3..2c75cbb9 100644
--- a/eval_protocol/training/gepa_utils.py
+++ b/eval_protocol/training/gepa_utils.py
@@ -199,7 +199,7 @@ def metric(
                 # No running loop - safe to use asyncio.run
                 evaluated_row = asyncio.run(result)
         else:
-            evaluated_row = result  # type: ignore[reportAssignmentType]
+            evaluated_row = result  # pyright: ignore[reportAssignmentType]
 
         # TODO: this is problematic. for groupwise, we will have to extend this to handle list[EvaluationRow]
 

From 8a2093b119377bf3de328078d5ffb6380f469c9b Mon Sep 17 00:00:00 2001
From: Shrey Modi <shreycricket10@gmail.com>
Date: Tue, 9 Dec 2025 13:19:50 -0800
Subject: [PATCH 10/15] updates

---
 eval_protocol/training/gepa_trainer.py | 30 +++++++++++++++++++++++
 eval_protocol/training/gepa_utils.py   | 34 +++++++++++++++++++++++---
 2 files changed, 61 insertions(+), 3 deletions(-)

diff --git a/eval_protocol/training/gepa_trainer.py b/eval_protocol/training/gepa_trainer.py
index 08541062..869ed050 100644
--- a/eval_protocol/training/gepa_trainer.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -153,6 +153,22 @@ def __init__(
                 module_factory=module_factory,
             )
 
+        # Debug: Verify program structure
+        print("\n🔍 DEBUG [GEPATrainer] PROGRAM STRUCTURE:")
+        print(f"   Program type: {type(self.program).__name__}")
+        print(f"   Has .predict: {hasattr(self.program, 'predict')}")
+        if hasattr(self.program, "predict"):
+            print(f"   predict type: {type(self.program.predict).__name__}")
+            print(f"   predict.signature: {self.program.predict.signature}")
+            print(
+                f"   predict.signature.instructions (first 300 chars): {(self.program.predict.signature.instructions or '')[:300]}..."
+            )
+        print(f"   Named predictors: {[name for name, _ in self.program.named_predictors()]}")
+        for name, pred in self.program.named_predictors():
+            print(
+                f"     - '{name}': {pred.signature.instructions[:100] if pred.signature.instructions else 'None'}..."
+            )
+
         # Convert EP rows to DSPy Examples
         self.train_set: List[Example] = evaluation_rows_to_dspy_examples(train_rows, input_field, output_field)
         self.val_set: List[Example] = evaluation_rows_to_dspy_examples(val_rows, input_field, output_field)
@@ -348,6 +364,20 @@ def train(
                 marker = " 🏆" if i == results.best_idx else ""
                 print(f"   Candidate {i}: {score:.3f}{marker}")
 
+            # Show all candidate instructions
+            print("\n📝 ALL CANDIDATE INSTRUCTIONS:")
+            if hasattr(results, "candidates") and results.candidates:
+                for i, cand_prog in enumerate(results.candidates):
+                    marker = " 🏆 BEST" if i == results.best_idx else ""
+                    print(f"\n   --- Candidate {i}{marker} (score: {results.val_aggregate_scores[i]:.3f}) ---")
+                    # Get instructions from the candidate program
+                    for name, pred in cand_prog.named_predictors():
+                        instr = pred.signature.instructions or ""
+                        print(f"   Predictor '{name}' instructions (first 500 chars):")
+                        print(f"   {instr[:500]}...")
+                        if len(instr) > 500:
+                            print(f"   ... ({len(instr)} total chars)")
+
         optimized_instructions = self.get_optimized_system_prompt(optimized_program)
         print("\n🎯 OPTIMIZED SYSTEM PROMPT:")
         print("-" * 60)
diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
index 2c75cbb9..a4d66d76 100644
--- a/eval_protocol/training/gepa_utils.py
+++ b/eval_protocol/training/gepa_utils.py
@@ -110,6 +110,14 @@ def gold_and_pred_to_row(gold: Example, pred: Prediction) -> EvaluationRow:
 
     content = pred.get("answer", "")
 
+    # Debug: print conversion details (only first few)
+    import os
+
+    if os.environ.get("EP_DEBUG_GEPA"):
+        print("\n  [gold_and_pred_to_row] Converting:")
+        print(f"    gold.answer type: {type(gt)}, value preview: {str(gt)[:100]}...")
+        print(f"    pred.answer type: {type(content)}, value preview: {str(content)[:100]}...")
+
     return EvaluationRow(
         messages=[
             Message(role="assistant", content=str(content))
@@ -325,6 +333,14 @@ def create_single_turn_program(
             module_factory=lambda sig: MyCustomModule(sig)
         )
     """
+    print("\n" + "⚙️" * 20)
+    print("DEBUG [create_single_turn_program] CREATING DSPY MODULE")
+    print("⚙️" * 20)
+    print(f"  input_field: '{input_field}'")
+    print(f"  output_field: '{output_field}'")
+    print(f"  module_type: {module_type}")
+    print(f"  system_prompt (first 200 chars): {(system_prompt or '')[:200]}...")
+
     # Create the signature
     sig = create_signature(
         input_field=input_field,
@@ -334,8 +350,12 @@ def create_single_turn_program(
         output_desc=output_desc,
     )
 
+    print(f"\n  Created signature: {sig}")
+    print(f"  Signature instructions (first 200 chars): {(sig.instructions or '')[:200]}...")
+
     # Use custom factory if provided
     if module_factory is not None:
+        print("  Using custom module factory")
         return module_factory(sig)
 
     # Convert string to enum if needed
@@ -344,14 +364,22 @@ def create_single_turn_program(
 
     # Create the appropriate module type
     if module_type == DSPyModuleType.PREDICT:
-        return dspy.Predict(sig)
+        program = dspy.Predict(sig)
     elif module_type == DSPyModuleType.CHAIN_OF_THOUGHT:
-        return dspy.ChainOfThought(sig)
+        program = dspy.ChainOfThought(sig)
     elif module_type == DSPyModuleType.PROGRAM_OF_THOUGHT:
-        return dspy.ProgramOfThought(sig)
+        program = dspy.ProgramOfThought(sig)
     else:
         raise ValueError(f"Unknown module type: {module_type}")
 
+    print(f"\n  Created module: {type(program).__name__}")
+    print(f"  Named predictors: {[name for name, _ in program.named_predictors()]}")
+    for name, pred in program.named_predictors():
+        print(f"    '{name}' signature.instructions (first 200 chars): {(pred.signature.instructions or '')[:200]}...")
+    print("⚙️" * 20 + "\n")
+
+    return program
+
 
 def configure_dspy_lm(ep_params: EPParameters) -> None:
     """

From ce61cadde855e7761df3d0856073275158d39c3d Mon Sep 17 00:00:00 2001
From: Shrey Modi <shreycricket10@gmail.com>
Date: Thu, 11 Dec 2025 14:03:53 -0800
Subject: [PATCH 11/15] cleaning up 1

---
 eval_protocol/models.py                |   2 +
 eval_protocol/trainable_gepa_design.md | 236 -------------------------
 eval_protocol/training/gepa_trainer.py | 155 +---------------
 eval_protocol/training/gepa_utils.py   |  50 ------
 eval_protocol/training/trainer.py      |   9 +-
 5 files changed, 17 insertions(+), 435 deletions(-)
 delete mode 100644 eval_protocol/trainable_gepa_design.md

diff --git a/eval_protocol/models.py b/eval_protocol/models.py
index 911c13b9..b869e140 100644
--- a/eval_protocol/models.py
+++ b/eval_protocol/models.py
@@ -1195,6 +1195,8 @@ class MCPMultiClientConfiguration(BaseModel):
 class EPParameters(BaseModel):
     """The parameters of an `@evaluation_test`. Used for trainable integrations."""
 
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
     completion_params: Any = None
     input_messages: Any = None
     input_dataset: Any = None
diff --git a/eval_protocol/trainable_gepa_design.md b/eval_protocol/trainable_gepa_design.md
deleted file mode 100644
index b66fb7e0..00000000
--- a/eval_protocol/trainable_gepa_design.md
+++ /dev/null
@@ -1,236 +0,0 @@
-## GEPA-training Interface Design for Eval Protocol
-
-### Goals
-
-- **Tunable prompts for existing benchmarks**: Allow benchmarks like `test_aime25.py` and `test_gpqa.py` to expose parts of their configuration (e.g., system prompts) as training parameters, without changing their core evaluation logic.
-- **Tight coupling with `@evaluation_test`**: Reuse the same rollout configuration, datasets, and metrics that are already defined via `evaluation_test`, instead of duplicating that configuration in a separate training API.
-- **GEPA as one optimizer backend**: Provide a clean integration point for GEPA (and potentially other optimizers later) without requiring benchmarks to depend on DSPy or GEPA directly.
-
-### High-Level Architecture
-
-- **Benchmark file (e.g., `test_aime25.py`)**
-  - Continues to define:
-    - Dataset adapter (`aime2025_dataset_adapter`).
-    - `@evaluation_test(...)`-decorated function (e.g., `test_aime25_pointwise`) that:
-      - Uses `SingleTurnRolloutProcessor` (or another processor).
-      - Computes per-row metrics and sets `row.evaluation_result`.
-  - Adds *optional* training wiring at the bottom, under `if __name__ == "__main__":`, that:
-    - Imports a training/core API from `eval_protocol.training`.
-    - Specifies what is tunable (e.g., the system prompt) and how to adapt rows using a candidate.
-    - Invokes a train routine (GEPA-based or otherwise).
-
-- **Training core**
-  - Provides a single central abstraction:
-    - **`EPParameters`**: Encapsulates everything `evaluation_test` knows about the eval in a structured form:
-      - One field for every parameter that `evaluation_test` accepts (dataset sources, adapters, completion params, rollout processor, aggregation, thresholds, etc.), after parsing/env overrides.
-    - **Candidate representation**: Start with `dict[str, str]` (e.g., `{"system_prompt": "..."}`), anticipating future extensions (few-shot examples, tool docs, etc.).
-  - Includes helper utilities to:
-    - Build an `EPParameters` instance by introspecting an `@evaluation_test`-decorated function.
-    - Run a single candidate or a batch of candidates through the full rollout + evaluation pipeline, returning aggregate scores (and optionally per-row scores).
-
-- **GEPA adapter (e.g., `eval_protocol/training/gepa_adapter.py`)**
-  - Wraps the training core and GEPA’s API:
-    - Accepts:
-      - An `EPConfig`.
-      - A candidate space definition (for now, implicit via `dict[str, str]` keys).
-      - GEPA configuration (budget, reflection model, seed, component selection strategy, etc.).
-    - Provides:
-      - A GEPA-compatible metric interface that:
-        - Given a candidate, uses `EPConfig` (and benchmark-specific logic such as a custom `dataset_adapter`) to:
-          - Construct or adapt rows for that candidate.
-          - Run rollouts (reusing the same processors and params as the test).
-          - Compute scalar scores (e.g., mean exact-match over a batch).
-      - A training routine that returns:
-        - A `best_candidate: dict[str, str]`.
-        - Optional rich result object (e.g., mapping to `GEPAResult`, additional stats).
-
-### Relationship to `evaluation_test` and `__ep_params__`
-
-- Existing `evaluation_test` code will attach:
-
-```python
-ep_params: dict[str, Any] = {
-    "rollout_processor": rollout_processor,
-    "server_script_path": server_script_path,
-    "mcp_config_path": mcp_config_path,
-    "rollout_processor_kwargs": rollout_processor_kwargs,
-    "mode": mode,
-}
-setattr(dual_mode_wrapper, "__ep_params__", ep_params)
-```
-
-- Design direction:
-  - **Use `__ep_params__` as the single source of truth**.
-  - **`__ep_params__` should contain all effective `evaluation_test` parameters**, including:
-    - Parsed `completion_params` (after env overrides).
-    - Dataset sources (`input_dataset`, `input_rows`, dataloaders, and `dataset_adapter`), after `parse_ep_*` transforms.
-    - `aggregation_method`, `num_runs`, `max_dataset_rows`, etc.
-    - Rollout and mode information (processor, kwargs, concurrency limits, mode).
-  - The training core can then **directly convert `__ep_params__` into an `EPParameters` instance** without maintaining a separate training-only config.
-
-- Training core will expose:
-  - A factory like:
-
-    ```python
-    def build_ep_parameters_from_test(
-        test_fn: TestFunction,
-    ) -> EPParameters:
-        ...
-    ```
-
-  - This function:
-    - Reads `test_fn.__ep_params__`.
-    - Reconstructs how to:
-      - Load and preprocess the dataset.
-      - Configure the rollout processor (`RolloutProcessorConfig`).
-      - Run rollouts and then apply the row-level metric (by calling the decorated test function in a library mode).
-
-- Training code (e.g., `python test_aime25.py`) then becomes:
-  - Import the test function (e.g., `test_aime25_pointwise`).
-  - Build an `EPParameters` from it.
-  - Call into a GEPA-based trainer that uses the `EPParameters`.
-
-### TODO for derek to figure out: how to store the changing system prompts.
-
-- **Where tuned prompts live (storage format and location)**:
-  - GEPA already supports a `run_dir` for logging and checkpoints.
-  - We need to decide:
-    - Whether EP should:
-      - Treat `run_dir` as the canonical store and optionally add a small `best_candidate.json` there; or
-      - Provide an additional EP-level artifact format.
-  - For now, storage is left as an **explicit design TODO** and can be finalized once we have the core/adapter in place.
-
-### Work Split: Person A vs Person B
-
-#### Person A – training Core & `evaluation_test` Integration
-
-- **1. Extend `evaluation_test` metadata (no behavior change)**
-  - Populate a single `__ep_config__` dict on the decorated test function that includes:
-    - Dataset specification (paths / input_rows / dataloaders, `dataset_adapter`, `max_dataset_rows`, etc.) after `parse_ep_*`.
-    - Parsed `completion_params` (after env overrides like `parse_ep_completion_params_overwrite`).
-    - Rollout settings (`rollout_processor`, `rollout_processor_kwargs`, `mode`, `max_concurrent_rollouts`, `max_concurrent_evaluations`).
-    - Aggregation and threshold metadata.
-  - Ensure:
-    - Backwards compatibility for existing tests.
-    - Clear typing and docstrings to guide future use.
-
-- **2. Define core training abstractions in `eval_protocol/training/core.py`**
-  - Define:
-    - `EPConfig`:
-      - A field for every parameter `evaluation_test` accepts (dataset, adapters, completion params, rollout processor, aggregation, thresholds, etc.).
-      - Can be serialized/inspected for external tooling.
-    - Candidate type alias (initially `Candidate = dict[str, str]`).
-  - Implement:
-    - `build_ep_config_from_test(test_fn: TestFunction) -> EPConfig`.
-      - Reads `__ep_config__`.
-      - Reuses the same dataset and rollout logic as pytest, but in a library-friendly way (no pytest invocation).
-  - Helper(s) to:
-    - Run a single candidate over the dataset, possibly with:
-      - A subset of rows (train vs val split initially determined by the benchmark or EPConfig).
-      - A configurable aggregation method (mean score to start).
-
-- **3. Minimal tests and documentation for the core**
-  - Add unit/integration tests that:
-    - Use a tiny fake `@evaluation_test` function.
-    - Confirm `build_ep_config_from_test` produces a config that can:
-      - Load mock rows.
-      - Run a dummy rollout processor.
-      - Apply a simple metric to produce scores.
-  - Document (in this design file or a short README) how benchmarks should think about exposing tunable pieces (e.g., via custom dataset adapters or other wiring).
-
-#### Person B – GEPA Adapter & Benchmark Wiring
-
-- **4. Implement GEPA integration in `eval_protocol/training/gepa_adapter.py`**
-  - Define a small adapter API, e.g.:
-
-```python
-class GEPATrainer:
-    def __init__(self, spec: trainingBenchmarkSpec, inject_fn: InjectFn, ...gepa_config...):
-        ...
-
-    def train(self) -> tuple[Candidate, Any]:
-        """Run GEPA and return best candidate plus optional rich result."""
-```
-
-  - Inside, implement:
-    - Conversion from `(spec, inject_fn)` into a GEPA metric:
-      - For each candidate:
-        - Clone or map the base dataset rows, applying `inject_fn(candidate, row)`.
-        - Use the spec’s rollout runner + metric runner to compute per-example and aggregate scores.
-        - Return the aggregate score (and optional textual feedback) to GEPA.
-    - The call to `gepa.optimize(...)` with:
-      - `seed_candidate` constructed from the baseline configuration (e.g., default system prompt).
-      - Budget configuration (max metric calls / auto presets).
-      - Reflection config (reflection LM or other knobs) passed in via constructor.
-    - Mapping from `GEPAResult` (or equivalent) back into:
-      - `best_candidate: Candidate`.
-      - Optional rich result object (e.g., exposing Pareto-front stats).
-
-- **5. Wire a first benchmark: AIME 2025**
-  - In `eval_protocol/benchmarks/test_aime25.py`:
-    - Factor the row-scoring logic inside `test_aime25_pointwise` into a **reusable metric function** (pure function that sets `row.evaluation_result` given a rolled-out row).
-    - Decide how candidates should influence the evaluation:
-      - For example, by making the dataset adapter or message-construction logic candidate-aware (e.g., changing the system prompt).
-    - Add a `if __name__ == "__main__":` block that:
-      - Imports `test_aime25_pointwise` and builds an `EPConfig` via `build_ep_config_from_test`.
-      - Instantiates `GEPATrainer` with:
-        - The `EPConfig`.
-        - Initial GEPA config (budget, reflection model placeholder, seed).
-      - Calls `trainer.train()` and prints/logs the resulting `best_candidate` for now.
-    - Keep storage of tuned prompts as a TODO/extension point to be resolved later.
-
-- **6. Optional second benchmark: GPQA**
-  - Repeat step 5 for `test_gpqa.py`:
-    - Identify what’s tunable (system prompt, possibly chain-of-thought instructions).
-    - Extract metric logic into a reusable function.
-    - Add candidate-aware wiring (e.g., via dataset adapters) and an optional `__main__` entrypoint calling the same GEPA trainer.
-  - This will validate that:
-    - The abstractions generalize across tasks.
-    - No DSPy/GEPA-specific imports leak into benchmark files (other than a small, well-defined training API).
-
-### Coordination Notes
-
-- **Order of work**
-  - Person A should go first (or in parallel up to the point where `EPConfig` and `build_ep_config_from_test` are usable).
-  - Person B can stub against interfaces and adjust once Person A’s core is available.
-- **Integration checkpoints**
-  - After Person A lands the core + tests:
-    - Person B wires AIME with a very simple “optimizer” (even random search) to smoke-test the path before hooking up real GEPA.
-  - After GEPA integration works for AIME:
-    - Decide on the canonical way to treat GEPA’s `run_dir` and/or additional artifacts for tuned prompts.
-    - Optionally add a small helper that knows how to “run evaluation once with best GEPA candidate” for CI workflows.
-
-
-future:
-
-this is how gepa defines eval:
-
-def metric(
-    gold: Example,
-    pred: Prediction,
-    trace: Optional[DSPyTrace] = None,
-    pred_name: Optional[str] = None,
-    pred_trace: Optional[DSPyTrace] = None,
-) -> float | ScoreWithFeedback:
-    """
-    This function is called with the following arguments:
-    - gold: The gold example.
-    - pred: The predicted output.
-    - trace: Optional. The trace of the program's execution.
-    - pred_name: Optional. The name of the target predictor currently being optimized by GEPA, for which
-        the feedback is being requested.
-    - pred_trace: Optional. The trace of the target predictor's execution GEPA is seeking feedback for.
-
-    Note the `pred_name` and `pred_trace` arguments. During optimization, GEPA will call the metric to obtain
-    feedback for individual predictors being optimized. GEPA provides the name of the predictor in `pred_name`
-    and the sub-trace (of the trace) corresponding to the predictor in `pred_trace`.
-    If available at the predictor level, the metric should return {'score': float, 'feedback': str} corresponding
-    to the predictor.
-    If not available at the predictor level, the metric can also return a text feedback at the program level
-    (using just the gold, pred and trace).
-    If no feedback is returned, GEPA will use a simple text feedback consisting of just the score:
-    f"This trajectory got a score of {score}."
-    """
-    ...
-
-ideally generic way to turn evaluation_test into this.
diff --git a/eval_protocol/training/gepa_trainer.py b/eval_protocol/training/gepa_trainer.py
index 869ed050..1b670780 100644
--- a/eval_protocol/training/gepa_trainer.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -117,23 +117,6 @@ def __init__(
         # Extract the system prompt from the dataset (this is what GEPA will optimize!)
         self._initial_system_prompt = extract_system_prompt_from_rows(self._rows)
 
-        # Debug: Print initial setup info
-        print("\n" + "=" * 80)
-        print("GEPA TRAINER INITIALIZATION")
-        print("=" * 80)
-        print(f"\n📊 Dataset loaded: {len(self._rows)} total rows")
-        print(f"   - Train: {len(train_rows)} rows")
-        print(f"   - Val: {len(val_rows)} rows")
-        print(f"   - Test: {len(test_rows)} rows")
-        print("\n📝 Initial System Prompt (what GEPA will optimize):")
-        print("-" * 40)
-        print(
-            self._initial_system_prompt[:500] + "..."
-            if self._initial_system_prompt and len(self._initial_system_prompt) > 500
-            else self._initial_system_prompt
-        )
-        print("-" * 40)
-
         # Create or use provided DSPy program
         if program is not None:
             # Use the provided program directly
@@ -153,37 +136,11 @@ def __init__(
                 module_factory=module_factory,
             )
 
-        # Debug: Verify program structure
-        print("\n🔍 DEBUG [GEPATrainer] PROGRAM STRUCTURE:")
-        print(f"   Program type: {type(self.program).__name__}")
-        print(f"   Has .predict: {hasattr(self.program, 'predict')}")
-        if hasattr(self.program, "predict"):
-            print(f"   predict type: {type(self.program.predict).__name__}")
-            print(f"   predict.signature: {self.program.predict.signature}")
-            print(
-                f"   predict.signature.instructions (first 300 chars): {(self.program.predict.signature.instructions or '')[:300]}..."
-            )
-        print(f"   Named predictors: {[name for name, _ in self.program.named_predictors()]}")
-        for name, pred in self.program.named_predictors():
-            print(
-                f"     - '{name}': {pred.signature.instructions[:100] if pred.signature.instructions else 'None'}..."
-            )
-
         # Convert EP rows to DSPy Examples
         self.train_set: List[Example] = evaluation_rows_to_dspy_examples(train_rows, input_field, output_field)
         self.val_set: List[Example] = evaluation_rows_to_dspy_examples(val_rows, input_field, output_field)
         self.test_set: List[Example] = evaluation_rows_to_dspy_examples(test_rows, input_field, output_field)
 
-        # Debug: Print example info
-        print("\n📦 DSPy Examples created:")
-        print(f"   Input field: '{input_field}', Output field: '{output_field}'")
-        if self.train_set:
-            ex = self.train_set[0]
-            print("\n   Sample train example:")
-            print(f"   - {input_field}: {str(getattr(ex, input_field, ''))[:200]}...")
-            print(f"   - {output_field}: {str(getattr(ex, output_field, ''))}")
-        print("=" * 80 + "\n")
-
     def _load_dataset(self) -> List[EvaluationRow]:
         """
         Load the dataset from ep_params.
@@ -241,7 +198,13 @@ def get_optimized_system_prompt(self, optimized_program: Module) -> str:
         This can be used with EP's rollout processor via system_prompt_override.
         """
         # GEPA stores optimized instructions in the signature
-        return optimized_program.predict.signature.instructions
+        # Handle both PREDICT (has .signature directly) and ChainOfThought (has .predict.signature)
+        if hasattr(optimized_program, "signature"):
+            return optimized_program.signature.instructions  # pyright: ignore[reportAttributeAccessIssue]
+        elif hasattr(optimized_program, "predict") and hasattr(optimized_program.predict, "signature"):  # pyright: ignore[reportAttributeAccessIssue]
+            return optimized_program.predict.signature.instructions  # pyright: ignore[reportAttributeAccessIssue]
+        else:
+            raise ValueError("Could not find signature.instructions on the optimized program")
 
     def train(
         self,
@@ -302,39 +265,6 @@ def train(
         }
         gepa_args.update(gepa_kwargs or {})
 
-        print("\n" + "=" * 80)
-        print("GEPA TRAINING STARTED")
-        print("=" * 80)
-        print(f"📋 Program type: {type(self.program).__name__}")
-
-        # Get signature - ChainOfThought stores it in .predict.signature
-        sig = None
-        if hasattr(self.program, "signature"):
-            sig = self.program.signature
-        elif hasattr(self.program, "predict") and hasattr(self.program.predict, "signature"):
-            sig = self.program.predict.signature
-
-        if sig:
-            print(f"📋 Signature: {sig}")
-            print("📋 Initial Instructions:")
-            print("-" * 40)
-            print(sig.instructions if sig.instructions else "None")
-            print("-" * 40)
-        else:
-            print("📋 Signature: N/A")
-
-        print(f"📋 Train set size: {len(self.train_set)}")
-        print(f"📋 Val set size: {len(self.val_set)}")
-        print(f"📋 Test set size: {len(self.test_set)}")
-        print(f"📋 GEPA auto mode: {gepa_args.get('auto', 'N/A')}")
-        print(f"📋 Reflection minibatch size: {gepa_args.get('reflection_minibatch_size', 3)}")
-        print("=" * 80 + "\n")
-
-        # Enable verbose logging from DSPy/GEPA
-        import logging
-
-        logging.getLogger("dspy.teleprompt.gepa.gepa").setLevel(logging.INFO)
-
         optimizer = GEPA(
             metric=self.metric,
             **gepa_args,
@@ -346,55 +276,6 @@ def train(
             valset=self.val_set,
         )
 
-        print("\n" + "=" * 80)
-        print("GEPA TRAINING COMPLETE")
-        print("=" * 80)
-
-        # Print detailed results if track_stats was enabled
-        if hasattr(optimized_program, "detailed_results"):
-            results = optimized_program.detailed_results
-            print("\n📊 OPTIMIZATION STATS:")
-            print(f"   Total metric calls: {results.total_metric_calls}")
-            print(f"   Full val evals: {results.num_full_val_evals}")
-            print(f"   Best candidate index: {results.best_idx}")
-            print(f"   Best val score: {results.val_aggregate_scores[results.best_idx]:.3f}")
-
-            print("\n📈 ALL CANDIDATE SCORES:")
-            for i, score in enumerate(results.val_aggregate_scores):
-                marker = " 🏆" if i == results.best_idx else ""
-                print(f"   Candidate {i}: {score:.3f}{marker}")
-
-            # Show all candidate instructions
-            print("\n📝 ALL CANDIDATE INSTRUCTIONS:")
-            if hasattr(results, "candidates") and results.candidates:
-                for i, cand_prog in enumerate(results.candidates):
-                    marker = " 🏆 BEST" if i == results.best_idx else ""
-                    print(f"\n   --- Candidate {i}{marker} (score: {results.val_aggregate_scores[i]:.3f}) ---")
-                    # Get instructions from the candidate program
-                    for name, pred in cand_prog.named_predictors():
-                        instr = pred.signature.instructions or ""
-                        print(f"   Predictor '{name}' instructions (first 500 chars):")
-                        print(f"   {instr[:500]}...")
-                        if len(instr) > 500:
-                            print(f"   ... ({len(instr)} total chars)")
-
-        optimized_instructions = self.get_optimized_system_prompt(optimized_program)
-        print("\n🎯 OPTIMIZED SYSTEM PROMPT:")
-        print("-" * 60)
-        print(optimized_instructions)
-        print("-" * 60)
-
-        # Compare with initial
-        print("\n📝 COMPARISON:")
-        print(f"   Initial prompt length: {len(self._initial_system_prompt or '')} chars")
-        print(f"   Optimized prompt length: {len(optimized_instructions)} chars")
-        if self._initial_system_prompt != optimized_instructions:
-            print("   ✅ Prompt was CHANGED by GEPA")
-        else:
-            print("   ⚠️  Prompt was NOT changed (model may already be optimal or no failures to learn from)")
-
-        print("=" * 80 + "\n")
-
         return optimized_program
 
     def evaluate(
@@ -403,7 +284,7 @@ def evaluate(
         num_threads: int = 32,
         display_table: bool = True,
         display_progress: bool = True,
-    ) -> dspy.evaluate.EvaluationResult:
+    ) -> Any:  # Returns dspy.evaluate.EvaluationResult
         """
         Evaluate the optimized program on the test set using DSPy's Evaluate.
 
@@ -431,7 +312,7 @@ def evaluate_baseline(
         num_threads: int = 32,
         display_table: bool = True,
         display_progress: bool = True,
-    ) -> dspy.evaluate.EvaluationResult:
+    ) -> Any:  # Returns dspy.evaluate.EvaluationResult
         """
         Evaluate the unoptimized baseline program on the test set.
 
@@ -498,11 +379,6 @@ async def evaluate_with_ep(
         # Get optimized system prompt
         optimized_prompt = self.get_optimized_system_prompt(optimized_program)
 
-        print("\n" + "=" * 80)
-        print("RUNNING EP EVALUATION (with LLM proxy & tracing)")
-        print("=" * 80)
-        print(f"📋 Using optimized prompt ({len(optimized_prompt)} chars)")
-
         # Get rows to evaluate
         if use_test_set:
             # Reconstruct test rows from test_set examples
@@ -513,10 +389,8 @@ async def evaluate_with_ep(
                 seed=42,
             )
             rows_to_eval = test_rows
-            print(f"📊 Evaluating on TEST SET: {len(rows_to_eval)} rows")
         else:
             rows_to_eval = self._rows
-            print(f"📊 Evaluating on FULL DATASET: {len(rows_to_eval)} rows")
 
         # Inject optimized system prompt into rows
         modified_rows = self._inject_system_prompt(rows_to_eval, optimized_prompt)
@@ -545,16 +419,11 @@ async def evaluate_with_ep(
         rollout_processor = SingleTurnRolloutProcessor()
         rollout_processor.setup()
 
-        print("🚀 Running rollouts through EP infrastructure...")
-        print(f"   Model: {completion_params.get('model', 'N/A')}")
-
         try:
             # Execute rollouts
             tasks = rollout_processor(modified_rows, config)
             rolled_out_rows = await asyncio.gather(*tasks)
 
-            print(f"✅ Rollouts complete: {len(rolled_out_rows)} rows")
-
             # Run evaluation function on each row
             evaluated_rows = []
             scores = []
@@ -574,12 +443,6 @@ async def evaluate_with_ep(
             # Calculate aggregate score
             avg_score = sum(scores) / len(scores) if scores else 0.0
 
-            print("\n📊 EVALUATION RESULTS:")
-            print(f"   Total rows: {len(evaluated_rows)}")
-            print(f"   Aggregate score: {avg_score:.3f}")
-            print(f"   Passing: {sum(1 for s in scores if s >= 0.5)}/{len(scores)}")
-            print("=" * 80 + "\n")
-
             return {
                 "rows": evaluated_rows,
                 "score": avg_score,
diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
index a4d66d76..0e3df571 100644
--- a/eval_protocol/training/gepa_utils.py
+++ b/eval_protocol/training/gepa_utils.py
@@ -1,4 +1,3 @@
-import os
 from typing import Any, Optional, Tuple
 
 import dspy
@@ -110,14 +109,6 @@ def gold_and_pred_to_row(gold: Example, pred: Prediction) -> EvaluationRow:
 
     content = pred.get("answer", "")
 
-    # Debug: print conversion details (only first few)
-    import os
-
-    if os.environ.get("EP_DEBUG_GEPA"):
-        print("\n  [gold_and_pred_to_row] Converting:")
-        print(f"    gold.answer type: {type(gt)}, value preview: {str(gt)[:100]}...")
-        print(f"    pred.answer type: {type(content)}, value preview: {str(content)[:100]}...")
-
     return EvaluationRow(
         messages=[
             Message(role="assistant", content=str(content))
@@ -160,11 +151,6 @@ def ep_test_to_gepa_metric(
     import asyncio
     import inspect
 
-    # Counter for debugging
-    call_count = [0]
-    DEBUG_METRIC = True  # Set to False to disable metric debug output
-    DEBUG_VERBOSE = True  # Set to True to print ALL calls (can be very verbose!)
-
     def metric(
         gold: Example,
         pred: Prediction,
@@ -172,18 +158,6 @@ def metric(
         pred_name: Optional[str] = None,
         pred_trace: Optional[DSPyTrace] = None,
     ) -> ScoreWithFeedback:
-        call_count[0] += 1
-
-        should_print = DEBUG_METRIC and (DEBUG_VERBOSE or call_count[0] <= 3)
-
-        if should_print:
-            print(f"\n🔍 METRIC CALL #{call_count[0]}")
-            print("-" * 40)
-            print(f"   Gold (expected): {gold.get('answer', 'N/A')}")
-            print(f"   Pred (model):    {str(pred.get('answer', 'N/A'))[:200]}")
-            if hasattr(pred, "reasoning") and pred.reasoning:
-                print(f"   Reasoning:       {str(pred.reasoning)[:300]}...")
-
         row = gold_and_pred_to_row(gold, pred)
 
         # Call the test function - handle both sync and async
@@ -212,12 +186,6 @@ def metric(
         # TODO: this is problematic. for groupwise, we will have to extend this to handle list[EvaluationRow]
 
         score_result = row_to_prediction(evaluated_row)
-
-        if should_print:
-            print(f"   Score:           {score_result.score}")
-            print(f"   Feedback:        {str(score_result.feedback)[:200]}")
-            print("-" * 40)
-
         return score_result
 
     return metric
@@ -333,14 +301,6 @@ def create_single_turn_program(
             module_factory=lambda sig: MyCustomModule(sig)
         )
     """
-    print("\n" + "⚙️" * 20)
-    print("DEBUG [create_single_turn_program] CREATING DSPY MODULE")
-    print("⚙️" * 20)
-    print(f"  input_field: '{input_field}'")
-    print(f"  output_field: '{output_field}'")
-    print(f"  module_type: {module_type}")
-    print(f"  system_prompt (first 200 chars): {(system_prompt or '')[:200]}...")
-
     # Create the signature
     sig = create_signature(
         input_field=input_field,
@@ -350,12 +310,8 @@ def create_single_turn_program(
         output_desc=output_desc,
     )
 
-    print(f"\n  Created signature: {sig}")
-    print(f"  Signature instructions (first 200 chars): {(sig.instructions or '')[:200]}...")
-
     # Use custom factory if provided
     if module_factory is not None:
-        print("  Using custom module factory")
         return module_factory(sig)
 
     # Convert string to enum if needed
@@ -372,12 +328,6 @@ def create_single_turn_program(
     else:
         raise ValueError(f"Unknown module type: {module_type}")
 
-    print(f"\n  Created module: {type(program).__name__}")
-    print(f"  Named predictors: {[name for name, _ in program.named_predictors()]}")
-    for name, pred in program.named_predictors():
-        print(f"    '{name}' signature.instructions (first 200 chars): {(pred.signature.instructions or '')[:200]}...")
-    print("⚙️" * 20 + "\n")
-
     return program
 
 
diff --git a/eval_protocol/training/trainer.py b/eval_protocol/training/trainer.py
index 1008bb41..4bcb9bfc 100644
--- a/eval_protocol/training/trainer.py
+++ b/eval_protocol/training/trainer.py
@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from typing import Any
 
 from eval_protocol.pytest.types import TestFunction
 
@@ -8,9 +9,11 @@ def __init__(self, test_fn: TestFunction):
         self.test_fn = test_fn
 
     @abstractmethod
-    def train(self, *args, **kwargs): ...
+    def train(self, *args: Any, **kwargs: Any) -> Any:
+        """Run training and return the optimized model/program."""
+        ...
 
     @abstractmethod
-    def evaluate(self, *args, **kwargs):
-        # evaluation logic possibly can be shared since it's EP. TBD
+    def evaluate(self, *args: Any, **kwargs: Any) -> Any:
+        """Evaluate the optimized model/program."""
         ...

From 7ddfceb5ed826d9e50d0c2d7ffe2d031783f8411 Mon Sep 17 00:00:00 2001
From: Derek Xu <xzrderek@gmail.com>
Date: Fri, 12 Dec 2025 12:35:16 -0800
Subject: [PATCH 12/15] undo

---
 eval_protocol/benchmarks/test_aime25.py | 86 ++-----------------------
 1 file changed, 4 insertions(+), 82 deletions(-)

diff --git a/eval_protocol/benchmarks/test_aime25.py b/eval_protocol/benchmarks/test_aime25.py
index 6994a0ca..6eb785a7 100644
--- a/eval_protocol/benchmarks/test_aime25.py
+++ b/eval_protocol/benchmarks/test_aime25.py
@@ -12,8 +12,6 @@
     SingleTurnRolloutProcessor,
 )
 from eval_protocol.pytest.evaluation_test import evaluation_test
-from eval_protocol.training import GEPATrainer
-from eval_protocol.training.gepa_utils import build_reflection_lm
 
 SYSTEM_PROMPT = (
     "You are a helpful math assistant. Please reason step by step, and put your final answer within \\boxed{...}."
@@ -63,44 +61,6 @@ def _normalize_to_int_or_none(s: Optional[str]) -> Optional[int]:
         return None
 
 
-def _build_feedback_text(
-    *,
-    extracted_int: Optional[int],
-    gt_int: Optional[int],
-    is_valid: bool,
-    raw_model_answer: str,
-    ground_truth: Optional[str],
-) -> str:
-    """
-    Build a feedback string similar in spirit to the GEPA `metric_with_feedback`.
-
-    Cases:
-    - Parse failure (model or gold): explain integer formatting and show correct answer.
-    - Correct: "Your answer is correct. The correct answer is '...'."
-    - Incorrect: "Your answer is incorrect. The correct answer is '...'."
-    """
-    correct_answer_display = str(gt_int if gt_int is not None else (ground_truth or ""))
-
-    if not is_valid:
-        # Could not parse either the model answer or the gold answer as an integer.
-        feedback_text = (
-            "The final answer must be a valid integer and nothing else. "
-            f"You responded with '{raw_model_answer}', which couldn't be parsed as a python integer. "
-            "Please ensure your answer is a valid integer without any additional text or formatting."
-        )
-        if correct_answer_display:
-            feedback_text += f" The correct answer is '{correct_answer_display}'."
-        return feedback_text
-
-    if extracted_int == gt_int:
-        return f"Your answer is correct. The correct answer is '{correct_answer_display}'."
-    else:
-        return f"Your answer is incorrect. The correct answer is '{correct_answer_display}'."
-
-    # TODO: our dataset does not contain written solutions, so we cannot provide feedback on the solution. maybe need to add it later.
-    # they're using https://huggingface.co/datasets/AI-MO/aimo-validation-aime
-
-
 def aime2025_dataset_adapter(rows: List[Dict[str, Any]]) -> List[EvaluationRow]:
     converted: List[EvaluationRow] = []
     for r in rows:
@@ -123,14 +83,15 @@ def aime2025_dataset_adapter(rows: List[Dict[str, Any]]) -> List[EvaluationRow]:
     completion_params=[
         {
             "max_tokens": 131000,
-            "model": "fireworks_ai/accounts/fireworks/models/deepseek-v3p1-terminus",
+            "extra_body": {"reasoning_effort": "low"},
+            "model": "fireworks_ai/accounts/fireworks/models/gpt-oss-120b",
         }
     ],
     rollout_processor=SingleTurnRolloutProcessor(),
     aggregation_method="mean",
     passed_threshold=0.8,
     num_runs=8,
-    max_dataset_rows=None,  # Use full dataset
+    max_dataset_rows=2,
     max_concurrent_rollouts=4,
     mode="pointwise",
 )
@@ -163,49 +124,10 @@ def test_aime25_pointwise(row: EvaluationRow) -> EvaluationRow:
         )
     }
 
-    feedback_text = _build_feedback_text(
-        extracted_int=extracted_int,
-        gt_int=gt_int,
-        is_valid=is_valid,
-        raw_model_answer=content_str,
-        ground_truth=str(row.ground_truth),
-    )
-
     row.evaluation_result = EvaluateResult(
         score=score,
-        reason=feedback_text,
+        reason=("Answer correct" if score == 1.0 else "Answer incorrect"),
         is_score_valid=is_valid,
         metrics=metrics,
     )
     return row
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    trainer = GEPATrainer(
-        test_aime25_pointwise,
-        train_ratio=0.5,  # 50% for training (15 problems)
-        val_ratio=0.3,  # 30% for validation (9 problems)
-        # test_ratio = 20% (6 problems) - calculated automatically
-    )
-
-    # Use same Fireworks model for both main and reflection
-    reflection_lm = build_reflection_lm("fireworks_ai/accounts/fireworks/models/deepseek-v3p1-terminus")
-
-    optimized_program = trainer.train(
-        num_threads=4,  # Reduced from 32 to avoid API timeouts
-        track_stats=True,
-        reflection_minibatch_size=5,  # Reduced to limit concurrent requests
-        reflection_lm=reflection_lm,
-    )
-
-    # Option 1: Quick DSPy evaluation (doesn't use EP infrastructure)
-    print("\n=== DSPy Evaluation ===")
-    print(trainer.evaluate(optimized_program))
-
-    # Option 2: Full EP evaluation (uses LLM proxy, Fireworks tracing, etc.)
-    # This goes through the normal @evaluation_test pipeline
-    print("\n=== EP Evaluation (with tracing) ===")
-    results = trainer.run_ep_evaluation(optimized_program)
-    print(f"Final EP Score: {results['score']:.3f}")

From c04acf8c4a0d00a04a119b542883157610a4cd46 Mon Sep 17 00:00:00 2001
From: Shrey Modi <shreycricket10@gmail.com>
Date: Mon, 15 Dec 2025 12:02:51 -0600
Subject: [PATCH 13/15] fixes

---
 .../pytest/integrations/openenv_trl_vllm.py   | 11 ++++---
 eval_protocol/training/gepa_trainer.py        | 28 ++++++++---------
 eval_protocol/training/gepa_utils.py          | 30 ++++++++++++++-----
 3 files changed, 43 insertions(+), 26 deletions(-)

diff --git a/eval_protocol/pytest/integrations/openenv_trl_vllm.py b/eval_protocol/pytest/integrations/openenv_trl_vllm.py
index 3f204680..9db3ce3d 100644
--- a/eval_protocol/pytest/integrations/openenv_trl_vllm.py
+++ b/eval_protocol/pytest/integrations/openenv_trl_vllm.py
@@ -121,10 +121,13 @@ def rollout_func(prompts: List[str], trainer) -> Dict[str, List]:
 
             eval_func = candidate_tests[0]
             ep_eval_func = eval_func  # used later after rollouts complete
-            ep_params: Dict[str, Any] = getattr(eval_func, "__ep_params__", {})
-            ep_rollout_processor = ep_params.get("rollout_processor")
-            ep_rollout_processor_kwargs = ep_params.get("rollout_processor_kwargs") or {}
-            ep_mcp_config_path = ep_params.get("mcp_config_path") or ""
+            ep_params = getattr(eval_func, "__ep_params__", None)
+            # ep_params is an EPParameters model (Pydantic), use attribute access
+            ep_rollout_processor = getattr(ep_params, "rollout_processor", None) if ep_params else None
+            ep_rollout_processor_kwargs = (
+                (getattr(ep_params, "rollout_processor_kwargs", None) or {}) if ep_params else {}
+            )
+            ep_mcp_config_path = (getattr(ep_params, "mcp_config_path", None) or "") if ep_params else ""
             logger.info(
                 "[OpenEnvVLLM] Loaded eval test '%s' with rollout_processor=%s",
                 getattr(eval_func, "__name__", str(eval_func)),
diff --git a/eval_protocol/training/gepa_trainer.py b/eval_protocol/training/gepa_trainer.py
index 1b670780..d4fa64b3 100644
--- a/eval_protocol/training/gepa_trainer.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -10,6 +10,9 @@
 
 from eval_protocol.models import EPParameters, EvaluationRow, Message
 from eval_protocol.pytest.types import TestFunction, RolloutProcessorConfig
+from eval_protocol.pytest.default_single_turn_rollout_process import SingleTurnRolloutProcessor
+from eval_protocol.pytest.execution import execute_pytest
+from eval_protocol.dataset_logger import default_logger
 from eval_protocol.training.trainer import Trainer
 from eval_protocol.training.utils import build_ep_parameters_from_test
 from eval_protocol.training.gepa_utils import (
@@ -98,12 +101,15 @@ def __init__(
         # Store configuration
         self._input_field = input_field
         self._output_field = output_field
+        self._train_ratio = train_ratio
+        self._val_ratio = val_ratio
+        self._seed = seed
 
         # Configure DSPy to use the same LLM as EP
         configure_dspy_lm(self.ep_params)
 
-        # Wrap the EP test function as a GEPA metric
-        self.metric = ep_test_to_gepa_metric(test_fn)
+        # Wrap the EP test function as a GEPA metric (with configured field names)
+        self.metric = ep_test_to_gepa_metric(test_fn, input_field, output_field)
 
         # Load and split the dataset
         self._rows: List[EvaluationRow] = self._load_dataset()
@@ -113,6 +119,10 @@ def __init__(
             val_ratio=val_ratio,
             seed=seed,
         )
+        # Store original EvaluationRow objects for later use in evaluate_with_ep
+        self._train_rows: List[EvaluationRow] = train_rows
+        self._val_rows: List[EvaluationRow] = val_rows
+        self._test_rows: List[EvaluationRow] = test_rows
 
         # Extract the system prompt from the dataset (this is what GEPA will optimize!)
         self._initial_system_prompt = extract_system_prompt_from_rows(self._rows)
@@ -372,23 +382,13 @@ async def evaluate_with_ep(
             - 'score': Aggregate score
             - 'optimized_prompt': The prompt used for evaluation
         """
-        from eval_protocol.pytest.default_single_turn_rollout_process import SingleTurnRolloutProcessor
-        from eval_protocol.pytest.execution import execute_pytest
-        from eval_protocol.logging import default_logger
-
         # Get optimized system prompt
         optimized_prompt = self.get_optimized_system_prompt(optimized_program)
 
         # Get rows to evaluate
         if use_test_set:
-            # Reconstruct test rows from test_set examples
-            _, _, test_rows = train_val_test_split(
-                self._rows,
-                train_ratio=0.5,  # Match the ratio used in training
-                val_ratio=0.3,
-                seed=42,
-            )
-            rows_to_eval = test_rows
+            # Use stored test rows (same split from __init__)
+            rows_to_eval = self._test_rows
         else:
             rows_to_eval = self._rows
 
diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
index 0e3df571..42ebb13d 100644
--- a/eval_protocol/training/gepa_utils.py
+++ b/eval_protocol/training/gepa_utils.py
@@ -91,23 +91,30 @@ def build_reflection_lm(reflection_lm_name: str) -> LM:
         return dspy.LM(model=reflection_lm_name)
 
 
-def gold_and_pred_to_row(gold: Example, pred: Prediction) -> EvaluationRow:
+def gold_and_pred_to_row(
+    gold: Example,
+    pred: Prediction,
+    input_field: str = "problem",
+    output_field: str = "answer",
+) -> EvaluationRow:
     """
     Convert a GEPA (gold, pred) pair into an EvaluationRow for an EP `@evaluation_test`.
 
-    Assumptions (aligned with common DSPy usage):
-    - `gold.answer` holds the ground-truth answer.
-    - `pred.answer` holds the model's final answer text.
+    Args:
+        gold: The ground-truth example
+        pred: The model's prediction
+        input_field: Name of the input field in the DSPy signature
+        output_field: Name of the output field in the DSPy signature
 
     Note: ground_truth is preserved in its original type (list, dict, str, etc.)
     to support structured comparisons like SQL result matching.
     """
-    gt = gold.get("answer", None)
+    gt = gold.get(output_field, None)
     # Preserve original type - don't convert to string!
     # This is important for SQL evaluators that expect list[dict] results
     ground_truth = gt
 
-    content = pred.get("answer", "")
+    content = pred.get(output_field, "")
 
     return EvaluationRow(
         messages=[
@@ -135,13 +142,20 @@ def row_to_prediction(row: EvaluationRow) -> ScoreWithFeedback:
 
 def ep_test_to_gepa_metric(
     test_fn: TestFunction,
+    input_field: str = "problem",
+    output_field: str = "answer",
 ) -> GEPAFeedbackMetric:
     """
     Adapter: convert an EP-style `test_fn(row: EvaluationRow) -> EvaluationRow` into
     a GEPAFeedbackMetric-compatible callable.
 
+    Args:
+        test_fn: The EP evaluation test function
+        input_field: Name of the input field in the DSPy signature (default: "problem")
+        output_field: Name of the output field in the DSPy signature (default: "answer")
+
     The resulting metric:
-    - Constructs an EvaluationRow from (gold, pred) using a simple heuristic.
+    - Constructs an EvaluationRow from (gold, pred) using the configured field names.
     - Applies the EP test_fn to populate `row.evaluation_result`.
     - Returns a dspy.Prediction(score, feedback) derived from that result.
 
@@ -158,7 +172,7 @@ def metric(
         pred_name: Optional[str] = None,
         pred_trace: Optional[DSPyTrace] = None,
     ) -> ScoreWithFeedback:
-        row = gold_and_pred_to_row(gold, pred)
+        row = gold_and_pred_to_row(gold, pred, input_field, output_field)
 
         # Call the test function - handle both sync and async
         result = test_fn(row)  # pyright: ignore

From 3336b908b04e909eacd9f67ca76f400e9c5da967 Mon Sep 17 00:00:00 2001
From: Shrey Modi <shreycricket10@gmail.com>
Date: Mon, 15 Dec 2025 12:12:47 -0600
Subject: [PATCH 14/15] fix

---
 eval_protocol/training/gepa_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eval_protocol/training/gepa_utils.py b/eval_protocol/training/gepa_utils.py
index 42ebb13d..15f30681 100644
--- a/eval_protocol/training/gepa_utils.py
+++ b/eval_protocol/training/gepa_utils.py
@@ -359,7 +359,7 @@ def configure_dspy_lm(ep_params: EPParameters) -> None:
 
     # Handle completion_params being a list (for sweeps) - use the first one
     if isinstance(raw_params, list):
-        completion_params = raw_params[0] if raw_params else {}
+        completion_params = (raw_params[0] if raw_params else None) or {}
     else:
         completion_params = raw_params or {}
 

From 7b3c4206495a963a93a5d0c69e03ba1c2e540388 Mon Sep 17 00:00:00 2001
From: Shrey Modi <shreycricket10@gmail.com>
Date: Mon, 15 Dec 2025 12:39:06 -0600
Subject: [PATCH 15/15] updated

---
 eval_protocol/models.py                 |  1 +
 eval_protocol/pytest/evaluation_test.py |  1 +
 eval_protocol/training/gepa_trainer.py  | 62 +++++++++++++++++++++++--
 3 files changed, 59 insertions(+), 5 deletions(-)

diff --git a/eval_protocol/models.py b/eval_protocol/models.py
index b869e140..a90fe8ac 100644
--- a/eval_protocol/models.py
+++ b/eval_protocol/models.py
@@ -1205,6 +1205,7 @@ class EPParameters(BaseModel):
     dataset_adapter: Optional[Callable[..., Any]] = None
     rollout_processor: Any = None
     rollout_processor_kwargs: Dict[str, Any] | None = None
+    evaluation_test_kwargs: Any = None
     aggregation_method: Any = Field(default="mean")
     passed_threshold: Any = None
     disable_browser_open: bool = False
diff --git a/eval_protocol/pytest/evaluation_test.py b/eval_protocol/pytest/evaluation_test.py
index 82065517..4a7ea88a 100644
--- a/eval_protocol/pytest/evaluation_test.py
+++ b/eval_protocol/pytest/evaluation_test.py
@@ -706,6 +706,7 @@ async def _collect_result(config, lst):
             dataset_adapter=dataset_adapter,
             rollout_processor=rollout_processor,
             rollout_processor_kwargs=rollout_processor_kwargs,
+            evaluation_test_kwargs=evaluation_test_kwargs,
             aggregation_method=aggregation_method,
             passed_threshold=passed_threshold,
             disable_browser_open=disable_browser_open,
diff --git a/eval_protocol/training/gepa_trainer.py b/eval_protocol/training/gepa_trainer.py
index d4fa64b3..d91efe67 100644
--- a/eval_protocol/training/gepa_trainer.py
+++ b/eval_protocol/training/gepa_trainer.py
@@ -157,14 +157,29 @@ def _load_dataset(self) -> List[EvaluationRow]:
 
         Supports:
         - input_rows: Pre-constructed EvaluationRow objects
+            - Can be List[EvaluationRow] (direct usage)
+            - Or Sequence[list[EvaluationRow]] (parameterized usage)
         - input_dataset: Paths to JSONL files (requires dataset_adapter)
         - input_messages: Raw message lists
+        - data_loaders: EvaluationDataLoader instances
         """
         ep = self.ep_params
 
         # Case 1: Pre-constructed rows
+        # Handle both direct List[EvaluationRow] and parameterized Sequence[list[EvaluationRow]]
         if ep.input_rows:
-            return list(ep.input_rows)
+            rows_input = ep.input_rows
+            # Check if it's a list of EvaluationRows (direct) or list of lists (parameterized)
+            if rows_input and isinstance(rows_input[0], EvaluationRow):
+                # Direct usage: List[EvaluationRow]
+                return list(rows_input)
+            else:
+                # Parameterized usage: Sequence[list[EvaluationRow]]
+                all_rows: List[EvaluationRow] = []
+                for rows_list in rows_input:
+                    if rows_list is not None:
+                        all_rows.extend(rows_list)
+                return all_rows
 
         # Case 2: Dataset paths with adapter
         if ep.input_dataset and ep.dataset_adapter:
@@ -183,17 +198,54 @@ def _load_dataset(self) -> List[EvaluationRow]:
             return ep.dataset_adapter(all_data)
 
         # Case 3: Input messages (convert to rows)
+        # Handle both direct List[List[Message]] and parameterized Sequence[list[list[Message]] | None]
         if ep.input_messages:
-            from eval_protocol.models import Message
+            rows: List[EvaluationRow] = []
+            messages_input = ep.input_messages
+
+            # Check if first element is a Message (direct list of conversations) or a list (parameterized)
+            if messages_input and messages_input[0]:
+                first_elem = messages_input[0]
+                # Check if it's List[Message] (a single conversation) or List[List[Message]]
+                if hasattr(first_elem, "role"):
+                    # It's a Message - so input is a single conversation List[Message]
+                    rows.append(EvaluationRow(messages=list(messages_input)))
+                elif first_elem and hasattr(first_elem[0], "role"):
+                    # It's List[List[Message]] - direct usage with multiple conversations
+                    for messages in messages_input:
+                        if messages:
+                            rows.append(EvaluationRow(messages=messages))
+                else:
+                    # Parameterized usage: Sequence[list[list[Message]] | None]
+                    for messages_list in messages_input:
+                        if messages_list is not None:
+                            for messages in messages_list:
+                                rows.append(EvaluationRow(messages=messages))
+            return rows
+
+        # Case 4: Data loaders
+        if ep.data_loaders:
+            from eval_protocol.data_loader.models import EvaluationDataLoader
 
             rows = []
-            for messages in ep.input_messages:
-                rows.append(EvaluationRow(messages=messages))
+            data_loaders = ep.data_loaders
+            data_loaders_list = (
+                [data_loaders] if isinstance(data_loaders, EvaluationDataLoader) else list(data_loaders)
+            )
+            for data_loader in data_loaders_list:
+                results = data_loader.load()
+                for result in results:
+                    rows.extend(result.rows)
+
+            # Apply max_dataset_rows limit
+            if ep.max_dataset_rows:
+                rows = rows[: ep.max_dataset_rows]
+
             return rows
 
         raise ValueError(
             "No dataset found in ep_params. "
-            "Provide input_rows, input_dataset (with dataset_adapter), or input_messages."
+            "Provide input_rows, input_dataset (with dataset_adapter), input_messages, or data_loaders."
         )
 
     @property