From d3fcc528b09a2fe93ef9c7dc9db7f38ffd41040f Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Tue, 6 Jan 2026 13:28:49 -0500
Subject: [PATCH 01/17] Add initial basic ConfigInfo model

---
 cmdstanpy/stanfit/metadata.py | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index 34c44c6b..1e88d8d6 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -8,7 +8,13 @@
 from typing import Any, Iterator, Literal
 
 import stanio
-from pydantic import BaseModel, field_validator, model_validator
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    NonNegativeInt,
+    field_validator,
+    model_validator,
+)
 
 from cmdstanpy.utils import stancsv
 
@@ -125,3 +131,15 @@ def validate_inv_metric_shape(self) -> MetricInfo:
                 raise ValueError("Dense inv_metric must be square")
 
         return self
+
+
+class ConfigInfo(BaseModel):
+    """Structured representation of a config JSON file output as part of a
+    Stan inference run. Only required fields are fully validated."""
+
+    model_config = ConfigDict(extra="allow")
+
+    stan_major_version: NonNegativeInt
+    stan_minor_version: NonNegativeInt
+    stan_patch_version: NonNegativeInt
+    model_name: str

From 81d08ab92eeeef662afebc3ccadfffac27a143af Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Fri, 30 Jan 2026 14:28:18 -0500
Subject: [PATCH 02/17] Add flattening and parsing logic for config JSON

---
 cmdstanpy/stanfit/metadata.py | 116 +++++++++++++++++++++++++++++++---
 1 file changed, 108 insertions(+), 8 deletions(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index 1e88d8d6..afece330 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -3,15 +3,16 @@
 from __future__ import annotations
 
 import copy
+import json
 import math
 import os
-from typing import Any, Iterator, Literal
+from typing import Annotated, Any, Iterator, Literal
 
 import stanio
 from pydantic import (
     BaseModel,
     ConfigDict,
-    NonNegativeInt,
+    Discriminator,
     field_validator,
     model_validator,
 )
@@ -133,13 +134,112 @@ def validate_inv_metric_shape(self) -> MetricInfo:
         return self
 
 
-class ConfigInfo(BaseModel):
-    """Structured representation of a config JSON file output as part of a
-    Stan inference run. Only required fields are fully validated."""
+class SampleConfig(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    method: Literal["sample"] = "sample"
+    algorithm: str
+    num_samples: int
+    num_warmup: int
+    save_warmup: bool = False
+    thin: int = 1
+    max_depth: int | None = None
+
+
+class OptimizeConfig(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    method: Literal["optimize"] = "optimize"
+    algorithm: str
+    save_iterations: bool = False
+    jacobian: bool = False
+
+
+class StanConfig(BaseModel):
+    """Common representation of a config JSON file output as part of a
+    Stan inference run. Separate method-specific config classes handle
+    the variation of output between methods."""
 
     model_config = ConfigDict(extra="allow")
 
-    stan_major_version: NonNegativeInt
-    stan_minor_version: NonNegativeInt
-    stan_patch_version: NonNegativeInt
     model_name: str
+    stan_major_version: str
+    stan_minor_version: str
+    stan_patch_version: str
+
+    method_config: Annotated[
+        SampleConfig | OptimizeConfig,
+        Discriminator('method'),
+    ]
+
+
+def flatten_value_dict(data: dict[str, Any]) -> dict[str, Any]:
+    """Recursively flatten CmdStan's nested value/subdict structure.
+
+    CmdStan uses a pattern where a field contains:
+        {"value": "val", "val": {"k1": v1, "k2": v2}}
+
+    This flattens it to the parent level as:
+        {"field": "val", "k1": v1, "k2": v2}
+
+    The flattening is applied recursively to any nested dicts.
+    """
+    result: dict[str, Any] = {}
+
+    for key, value in data.items():
+        if not isinstance(value, dict):
+            result[key] = value
+            continue
+
+        # Check if this is a value/subdict pattern
+        if 'value' in value:
+            value_name = value['value']
+            result[key] = value_name
+
+            # Get the nested dict matching the value name and flatten it
+            nested = value.get(value_name, {})
+            if isinstance(nested, dict):
+                # Recursively flatten the nested dict first
+                flattened_nested = flatten_value_dict(nested)
+                # Merge into result (nested keys go to parent level)
+                for nested_key, nested_value in flattened_nested.items():
+                    if nested_key not in result:
+                        result[nested_key] = nested_value
+        else:
+            # Regular dict without value pattern - recurse into it
+            result[key] = flatten_value_dict(value)
+
+    return result
+
+
+def flatten_config(data: dict[str, Any]) -> dict[str, Any]:
+    """Flatten nested CmdStan config JSON structure.
+
+    CmdStan outputs config JSON with deeply nested structure like:
+        {"method": {"value": "sample", "sample": {"num_samples": 1000, ...}}}
+
+    This flattens it to:
+        {"method_config": {"method": "sample", "num_samples": 1000, ...}, ...}
+    """
+    method_data = data.get('method')
+    if not isinstance(method_data, dict):
+        return data
+
+    result = {k: v for k, v in data.items() if k != "method"}
+    method_name = method_data.get('value')
+
+    # Build method_config from the method-specific nested dict
+    nested_method = method_data.get(method_name, {})
+    method_config = flatten_value_dict(nested_method)
+    method_config['method'] = method_name
+
+    result['method_config'] = method_config
+    return result
+
+
+def parse_config(json_data: str | bytes) -> StanConfig:
+    """Parse a CmdStan config JSON string into a StanConfig."""
+
+    raw = json.loads(json_data)
+    flat = flatten_config(raw)
+    return StanConfig.model_validate(flat)  # type: ignore

From b3b20ebd890e3e33ef7317ca8eaaad253515e890 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 31 Jan 2026 13:57:48 -0500
Subject: [PATCH 03/17] Update flatten_value_dict to clarify variable naming

---
 cmdstanpy/stanfit/metadata.py | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index afece330..a9eeff24 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -169,7 +169,7 @@ class StanConfig(BaseModel):
 
     method_config: Annotated[
         SampleConfig | OptimizeConfig,
-        Discriminator('method'),
+        Discriminator("method"),
     ]
 
 
@@ -186,28 +186,25 @@ def flatten_value_dict(data: dict[str, Any]) -> dict[str, Any]:
     """
     result: dict[str, Any] = {}
 
-    for key, value in data.items():
-        if not isinstance(value, dict):
-            result[key] = value
+    for key, val in data.items():
+        if not isinstance(val, dict):
+            result[key] = val
             continue
 
-        # Check if this is a value/subdict pattern
-        if 'value' in value:
-            value_name = value['value']
+        if "value" in val:
+            value_name = val['value']
             result[key] = value_name
 
             # Get the nested dict matching the value name and flatten it
-            nested = value.get(value_name, {})
+            nested = val.get(value_name, {})
             if isinstance(nested, dict):
-                # Recursively flatten the nested dict first
                 flattened_nested = flatten_value_dict(nested)
-                # Merge into result (nested keys go to parent level)
-                for nested_key, nested_value in flattened_nested.items():
+                for nested_key, nested_val in flattened_nested.items():
                     if nested_key not in result:
-                        result[nested_key] = nested_value
+                        result[nested_key] = nested_val
         else:
             # Regular dict without value pattern - recurse into it
-            result[key] = flatten_value_dict(value)
+            result[key] = flatten_value_dict(val)
 
     return result
 

From a5f8094b585c532a9e1b70b61889bfd6ebe5afb6 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 31 Jan 2026 14:13:00 -0500
Subject: [PATCH 04/17] Add PathfinderConfig

---
 cmdstanpy/stanfit/metadata.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index a9eeff24..2edba6cb 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -155,6 +155,16 @@ class OptimizeConfig(BaseModel):
     jacobian: bool = False
 
 
+class PathfinderConfig(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    method: Literal["pathfinder"] = "pathfinder"
+    num_draws: int = 1000
+    num_paths: int = 4
+    psis_resample: bool = True
+    calculate_lp: bool = True
+
+
 class StanConfig(BaseModel):
     """Common representation of a config JSON file output as part of a
     Stan inference run. Separate method-specific config classes handle
@@ -168,7 +178,7 @@ class StanConfig(BaseModel):
     stan_patch_version: str
 
     method_config: Annotated[
-        SampleConfig | OptimizeConfig,
+        SampleConfig | OptimizeConfig | PathfinderConfig,
         Discriminator("method"),
     ]
 

From d49c62c8868fbffabeb056ebf8616e9892ef003c Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 31 Jan 2026 14:24:14 -0500
Subject: [PATCH 05/17] Add LaplaceConfig

---
 cmdstanpy/stanfit/metadata.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index 2edba6cb..eab4a587 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -165,6 +165,15 @@ class PathfinderConfig(BaseModel):
     calculate_lp: bool = True
 
 
+class LaplaceConfig(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    method: Literal["laplace"] = "laplace"
+    mode: str
+    draws: int = 1000
+    jacobian: bool = True
+
+
 class StanConfig(BaseModel):
     """Common representation of a config JSON file output as part of a
     Stan inference run. Separate method-specific config classes handle
@@ -178,7 +187,7 @@ class StanConfig(BaseModel):
     stan_patch_version: str
 
     method_config: Annotated[
-        SampleConfig | OptimizeConfig | PathfinderConfig,
+        SampleConfig | OptimizeConfig | PathfinderConfig | LaplaceConfig,
         Discriminator("method"),
     ]
 

From 503a970d0a1a3c9f50b32293fb7b5863f198708a Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 31 Jan 2026 14:31:02 -0500
Subject: [PATCH 06/17] Add VariationalConfig

---
 cmdstanpy/stanfit/metadata.py | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index eab4a587..f2e34d63 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -174,6 +174,20 @@ class LaplaceConfig(BaseModel):
     jacobian: bool = True
 
 
+class VariationalConfig(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    method: Literal["variational"] = "variational"
+    algorithm: str
+    iter: int = 10000
+    grad_samples: int = 1
+    elbo_samples: int = 100
+    eta: float = 1.0
+    tol_rel_obj: float = 0.01
+    eval_elbo: int = 100
+    output_samples: int = 1000
+
+
 class StanConfig(BaseModel):
     """Common representation of a config JSON file output as part of a
     Stan inference run. Separate method-specific config classes handle
@@ -187,7 +201,11 @@ class StanConfig(BaseModel):
     stan_patch_version: str
 
     method_config: Annotated[
-        SampleConfig | OptimizeConfig | PathfinderConfig | LaplaceConfig,
+        SampleConfig
+        | OptimizeConfig
+        | PathfinderConfig
+        | LaplaceConfig
+        | VariationalConfig,
         Discriminator("method"),
     ]
 

From 08c6d0eb7fec8feb1d8ae03d6f181ec4d8678026 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 31 Jan 2026 14:43:16 -0500
Subject: [PATCH 07/17] Add GeneratedQuantitiesConfig

---
 cmdstanpy/stanfit/metadata.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index f2e34d63..1dac3b1d 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -188,6 +188,14 @@ class VariationalConfig(BaseModel):
     output_samples: int = 1000
 
 
+class GeneratedQuantitiesConfig(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    method: Literal["generate_quantities"] = "generate_quantities"
+    fitted_params: str
+    num_chains: int = 1
+
+
 class StanConfig(BaseModel):
     """Common representation of a config JSON file output as part of a
     Stan inference run. Separate method-specific config classes handle
@@ -205,7 +213,8 @@ class StanConfig(BaseModel):
         | OptimizeConfig
         | PathfinderConfig
         | LaplaceConfig
-        | VariationalConfig,
+        | VariationalConfig
+        | GeneratedQuantitiesConfig,
         Discriminator("method"),
     ]
 

From 63ca9a5693a57123056af42fb68c5445cfc8b157 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 15 Dec 2025 07:02:51 +0000
Subject: [PATCH 08/17] Bump actions/cache from 4 to 5

Bumps [actions/cache](https://github.com/actions/cache) from 4 to 5.
- [Release notes](https://github.com/actions/cache/releases)
- [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md)
- [Commits](https://github.com/actions/cache/compare/v4...v5)

---
updated-dependencies:
- dependency-name: actions/cache
  dependency-version: '5'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/main.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index bb340a8f..5de8657a 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -77,7 +77,7 @@ jobs:
       - name: CmdStan installation cacheing
         id: cache-cmdstan
         if: ${{ !startswith(needs.get-cmdstan-version.outputs.version, 'git:') }}
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cmdstan
           key: ${{ runner.os }}-cmdstan-${{ needs.get-cmdstan-version.outputs.version }}-${{ hashFiles('**/install_cmdstan.py') }}

From 8f62f2e3e3f6ec4a8cd83143806ebf73cff414a0 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 11 Apr 2026 20:53:52 -0400
Subject: [PATCH 09/17] Add first-pass CmdStanPathfinder rewrite

---
 cmdstanpy/model.py                            |   11 +-
 cmdstanpy/stanfit/__init__.py                 |   29 +-
 cmdstanpy/stanfit/gq.py                       |   31 +-
 cmdstanpy/stanfit/pathfinder.py               |  130 +-
 test/data/pathfinder/bernoulli-pathfinder.csv | 2033 +++++++++--------
 .../bernoulli-pathfinder_config.json          |   48 +
 test/test_metadata.py                         |  260 ++-
 7 files changed, 1440 insertions(+), 1102 deletions(-)
 create mode 100644 test/data/pathfinder/bernoulli-pathfinder_config.json

diff --git a/cmdstanpy/model.py b/cmdstanpy/model.py
index ece9fd2e..0a688a18 100644
--- a/cmdstanpy/model.py
+++ b/cmdstanpy/model.py
@@ -1040,7 +1040,10 @@ def generate_quantities(
             ),
         ):
             fit_object = previous_fit
-            fit_csv_files = previous_fit.runset.csv_files
+            if isinstance(previous_fit, CmdStanPathfinder):
+                fit_csv_files = [previous_fit.csv_file]
+            else:
+                fit_csv_files = previous_fit.runset.csv_files
         elif isinstance(previous_fit, list):
             if len(previous_fit) < 1:
                 raise ValueError(
@@ -1553,7 +1556,11 @@ def pathfinder(
                 ' '.join(runset.cmd(0)), runset.get_err_msgs()
             )
             raise RuntimeError(msg)
-        return CmdStanPathfinder(runset)
+        return CmdStanPathfinder.from_files(
+            csv_file=runset.csv_files[0],
+            config_file=runset.config_files[0],
+            stdout_file=runset.stdout_files[0],
+        )
 
     def log_prob(
         self,
diff --git a/cmdstanpy/stanfit/__init__.py b/cmdstanpy/stanfit/__init__.py
index 9f44d842..787d8238 100644
--- a/cmdstanpy/stanfit/__init__.py
+++ b/cmdstanpy/stanfit/__init__.py
@@ -7,7 +7,6 @@
     CmdStanArgs,
     LaplaceArgs,
     OptimizeArgs,
-    PathfinderArgs,
     SamplerArgs,
     VariationalArgs,
 )
@@ -256,21 +255,21 @@ def from_csv(
             )
             return CmdStanLaplace(runset, mode=mode)
         elif config_dict['method'] == 'pathfinder':
-            pathfinder_args = PathfinderArgs(
-                num_draws=config_dict['num_draws'],  # type: ignore
-                num_paths=config_dict['num_paths'],  # type: ignore
-            )
-            cmdstan_args = CmdStanArgs(
-                model_name=model,
-                model_exe=model,
-                chain_ids=None,
-                method_args=pathfinder_args,
+            if len(csvfiles) != 1:
+                raise ValueError(
+                    'Expecting a single Pathfinder Stan CSV file, '
+                    f'found {len(csvfiles)}'
+                )
+            csv_file = csvfiles[0]
+            config_file = os.path.splitext(csv_file)[0] + '_config.json'
+            if not os.path.exists(config_file):
+                raise ValueError(
+                    'Pathfinder config file not found at expected path: '
+                    f'{config_file}'
+                )
+            return CmdStanPathfinder.from_files(
+                csv_file=csv_file, config_file=config_file
             )
-            runset = RunSet(args=cmdstan_args)
-            runset._csv_files = csvfiles
-            for i in range(len(runset._retcodes)):
-                runset._set_retcode(i, 0)
-            return CmdStanPathfinder(runset)
         else:
             get_logger().warning(
                 'Unable to process CSV output files from method %s.',
diff --git a/cmdstanpy/stanfit/gq.py b/cmdstanpy/stanfit/gq.py
index 7046fd61..1937cc32 100644
--- a/cmdstanpy/stanfit/gq.py
+++ b/cmdstanpy/stanfit/gq.py
@@ -6,15 +6,8 @@
 from __future__ import annotations
 
 from collections import Counter
-from typing import (
-    Any,
-    Generic,
-    Hashable,
-    MutableMapping,
-    NoReturn,
-    TypeVar,
-    overload,
-)
+from collections.abc import Hashable
+from typing import Any, Generic, MutableMapping, NoReturn, TypeVar, overload
 
 import numpy as np
 import pandas as pd
@@ -239,7 +232,7 @@ def draws(
             drop_cols: list[int] = []
             for dup in dups:
                 drop_cols.extend(
-                    self.previous_fit._metadata.stan_vars[dup].columns()
+                    self.previous_fit.metadata.stan_vars[dup].columns()
                 )
 
         start_idx, _ = self._draws_start(inc_warmup)
@@ -333,10 +326,8 @@ def draws_pd(
                     gq_cols.extend(
                         self.column_names[info.start_idx : info.end_idx]
                     )
-                elif (
-                    inc_sample and var in self.previous_fit._metadata.stan_vars
-                ):
-                    info = self.previous_fit._metadata.stan_vars[var]
+                elif inc_sample and var in self.previous_fit.metadata.stan_vars:
+                    info = self.previous_fit.metadata.stan_vars[var]
                     mcmc_vars.extend(
                         self.previous_fit.column_names[
                             info.start_idx : info.end_idx
@@ -472,7 +463,7 @@ def draws_xr(
             for var in vars_list:
                 if var not in self._metadata.stan_vars:
                     if inc_sample and (
-                        var in self.previous_fit._metadata.stan_vars
+                        var in self.previous_fit.metadata.stan_vars
                     ):
                         mcmc_vars_list.append(var)
                         dup_vars.append(var)
@@ -481,7 +472,7 @@ def draws_xr(
         else:
             vars_list = list(self._metadata.stan_vars.keys())
             if inc_sample:
-                for var in self.previous_fit._metadata.stan_vars.keys():
+                for var in self.previous_fit.metadata.stan_vars.keys():
                     if var not in vars_list and var not in mcmc_vars_list:
                         mcmc_vars_list.append(var)
         for var in dup_vars:
@@ -490,7 +481,7 @@ def draws_xr(
         self._assemble_generated_quantities()
 
         num_draws = self.previous_fit.num_draws_sampling
-        sample_config = self.previous_fit._metadata.cmdstan_config
+        sample_config = self.previous_fit.metadata.cmdstan_config
         attrs: MutableMapping[Hashable, Any] = {
             "stan_version": f"{sample_config['stan_version_major']}."
             f"{sample_config['stan_version_minor']}."
@@ -518,7 +509,7 @@ def draws_xr(
             for var in mcmc_vars_list:
                 build_xarray_data(
                     data,
-                    self.previous_fit._metadata.stan_vars[var],
+                    self.previous_fit.metadata.stan_vars[var],
                     self.previous_fit.draws(inc_warmup=inc_warmup),
                 )
 
@@ -570,7 +561,7 @@ def stan_variable(self, var: str, **kwargs: bool) -> np.ndarray:
         CmdStanVB.stan_variable
         CmdStanLaplace.stan_variable
         """
-        model_var_names = self.previous_fit._metadata.stan_vars.keys()
+        model_var_names = self.previous_fit.metadata.stan_vars.keys()
         gq_var_names = self._metadata.stan_vars.keys()
         if not (var in model_var_names or var in gq_var_names):
             raise ValueError(
@@ -611,7 +602,7 @@ def stan_variables(self, **kwargs: bool) -> dict[str, np.ndarray]:
         CmdStanLaplace.stan_variables
         """
         result = {}
-        sample_var_names = self.previous_fit._metadata.stan_vars.keys()
+        sample_var_names = self.previous_fit.metadata.stan_vars.keys()
         gq_var_names = self._metadata.stan_vars.keys()
         for name in gq_var_names:
             result[name] = self.stan_variable(name, **kwargs)
diff --git a/cmdstanpy/stanfit/pathfinder.py b/cmdstanpy/stanfit/pathfinder.py
index 60f831cd..bc0339d6 100644
--- a/cmdstanpy/stanfit/pathfinder.py
+++ b/cmdstanpy/stanfit/pathfinder.py
@@ -2,30 +2,63 @@
 Container for the result of running Pathfinder.
 """
 
+from __future__ import annotations
+
+import os
+import shutil
+from dataclasses import dataclass, field
+from pathlib import Path
+
 import numpy as np
 
-from cmdstanpy.cmdstan_args import Method
-from cmdstanpy.stanfit.metadata import InferenceMetadata
-from cmdstanpy.stanfit.runset import RunSet
+from cmdstanpy.stanfit.metadata import (
+    InferenceMetadata,
+    PathfinderConfig,
+    parse_config,
+)
 from cmdstanpy.utils import stancsv
 
 
+@dataclass
 class CmdStanPathfinder:
     """
     Container for outputs from the Pathfinder algorithm.
     Created by :meth:`CmdStanModel.pathfinder()`.
     """
 
-    def __init__(self, runset: RunSet):
-        """Initialize object."""
-        if not runset.method == Method.PATHFINDER:
+    metadata: InferenceMetadata
+    model_name: str
+    csv_file: str
+    config: PathfinderConfig
+    config_file: str | None = None
+    stdout_file: str | None = None
+    _draws: np.ndarray = field(default_factory=lambda: np.array(()), init=False)
+
+    @classmethod
+    def from_files(
+        cls,
+        csv_file: str | os.PathLike,
+        config_file: str | os.PathLike,
+        stdout_file: str | os.PathLike | None = None,
+    ) -> CmdStanPathfinder:
+        with open(config_file) as f:
+            stan_config = parse_config(f.read())
+
+        if not isinstance(stan_config.method_config, PathfinderConfig):
+            conf_name = type(stan_config.method_config).__name__
             raise ValueError(
-                'Wrong runset method, expecting Pathfinder runset, '
-                'found method {}'.format(runset.method)
+                f"Did not find Pathfinder config, instead found: {conf_name}"
             )
-        self.runset = runset
-        self._draws: np.ndarray = np.array(())
-        self._metadata = InferenceMetadata.from_csv(self.runset.csv_files[0])
+
+        metadata = InferenceMetadata.from_csv(csv_file)
+        return cls(
+            metadata=metadata,
+            csv_file=str(csv_file),
+            model_name=stan_config.model_name,
+            config=stan_config.method_config,
+            config_file=str(config_file),
+            stdout_file=str(stdout_file) if stdout_file is not None else None,
+        )
 
     def create_inits(
         self, seed: int | None = None, chains: int = 4
@@ -49,42 +82,38 @@ def create_inits(
             draw = self._draws[idxs[0]]
             return {
                 name: var.extract_reshape(draw)
-                for name, var in self._metadata.stan_vars.items()
+                for name, var in self.metadata.stan_vars.items()
             }
         else:
             return [
                 {
                     name: var.extract_reshape(self._draws[idx])
-                    for name, var in self._metadata.stan_vars.items()
+                    for name, var in self.metadata.stan_vars.items()
                 }
                 for idx in idxs
             ]
 
     def __repr__(self) -> str:
-        rep = 'CmdStanPathfinder: model={}{}'.format(
-            self.runset.model,
-            self.runset._args.method_args.compose(0, cmd=[]),
-        )
-        rep = '{}\n csv_files:\n\t{}\n output_files:\n\t{}'.format(
-            rep,
-            '\n\t'.join(self.runset.csv_files),
-            '\n\t'.join(self.runset.stdout_files),
-        )
-        return rep
+        lines = [
+            f'CmdStanPathfinder: model={self.model_name}',
+            f' csv_file:\n\t{self.csv_file}',
+        ]
+        if self.config_file is not None:
+            lines.append(f' config_file:\n\t{self.config_file}')
+        if self.stdout_file is not None:
+            lines.append(f' output_file:\n\t{self.stdout_file}')
+        return '\n'.join(lines)
 
     def _assemble_draws(self) -> None:
         if self._draws.shape != (0,):
             return
 
-        csv_file = self.runset.csv_files[0]
         try:
-            *_, draws = stancsv.parse_comments_header_and_draws(
-                self.runset.csv_files[0]
-            )
+            *_, draws = stancsv.parse_comments_header_and_draws(self.csv_file)
             self._draws = stancsv.csv_bytes_list_to_numpy(draws)
         except Exception as exc:
             raise ValueError(
-                f"An error occurred when parsing Stan csv {csv_file}"
+                f"An error occurred when parsing Stan csv {self.csv_file}"
             ) from exc
 
     def stan_variable(self, var: str) -> np.ndarray:
@@ -109,7 +138,7 @@ def stan_variable(self, var: str) -> np.ndarray:
         """
         self._assemble_draws()
         try:
-            out: np.ndarray = self._metadata.stan_vars[var].extract_reshape(
+            out: np.ndarray = self.metadata.stan_vars[var].extract_reshape(
                 self._draws
             )
             return out
@@ -118,7 +147,7 @@ def stan_variable(self, var: str) -> np.ndarray:
             raise ValueError(
                 f'Unknown variable name: {var}\n'
                 'Available variables are '
-                + ", ".join(self._metadata.stan_vars.keys())
+                + ", ".join(self.metadata.stan_vars.keys())
             )
 
     def stan_variables(self) -> dict[str, np.ndarray]:
@@ -136,7 +165,7 @@ def stan_variables(self) -> dict[str, np.ndarray]:
         CmdStanLaplace.stan_variables
         """
         result = {}
-        for name in self._metadata.stan_vars:
+        for name in self.metadata.stan_vars:
             result[name] = self.stan_variable(name)
         return result
 
@@ -151,7 +180,7 @@ def method_variables(self) -> dict[str, np.ndarray]:
         self._assemble_draws()
         return {
             name: var.extract_reshape(self._draws)
-            for name, var in self._metadata.method_vars.items()
+            for name, var in self.metadata.method_vars.items()
         }
 
     def draws(self) -> np.ndarray:
@@ -181,15 +210,6 @@ def __getstate__(self) -> dict:
         self._assemble_draws()
         return self.__dict__
 
-    @property
-    def metadata(self) -> InferenceMetadata:
-        """
-        Returns object which contains CmdStan configuration as well as
-        information about the names and structure of the inference method
-        and model output variables.
-        """
-        return self._metadata
-
     @property
     def column_names(self) -> tuple[str, ...]:
         """
@@ -198,7 +218,7 @@ def column_names(self) -> tuple[str, ...]:
         and quantities of interest. Corresponds to Stan CSV file header row,
         with names munged to array notation, e.g. `beta[1]` not `beta.1`.
         """
-        return self._metadata.column_names
+        return self.metadata.column_names
 
     @property
     def is_resampled(self) -> bool:
@@ -207,22 +227,32 @@ def is_resampled(self) -> bool:
         approximations, False otherwise.
         """
         return (
-            self._metadata.cmdstan_config.get("num_paths", 4) > 1
-            and self._metadata.cmdstan_config.get('psis_resample', 1)
-            in (1, 'true')
-            and self._metadata.cmdstan_config.get('calculate_lp', 1)
-            in (1, 'true')
+            self.config.num_paths > 1
+            and self.config.psis_resample
+            and self.config.calculate_lp
         )
 
     def save_csvfiles(self, dir: str | None = None) -> None:
         """
-        Move output CSV files to specified directory.
+        Move output CSV file, and any associated config and stdout files,
+        to the specified directory. Updates the corresponding attributes on
+        this object to point at the new locations.
 
         :param dir: directory path
 
         See Also
         --------
-        stanfit.RunSet.save_csvfiles
         cmdstanpy.from_csv
         """
-        self.runset.save_csvfiles(dir)
+        dest = Path(dir) if dir is not None else Path.cwd()
+        dest.mkdir(parents=True, exist_ok=True)
+
+        for attr in ('csv_file', 'config_file', 'stdout_file'):
+            src = getattr(self, attr)
+            if src is None:
+                continue
+            dst = dest / Path(src).name
+            if dst.exists():
+                raise ValueError(f'File exists, not overwriting: {dst}')
+            shutil.move(src, dst)
+            setattr(self, attr, str(dst))
diff --git a/test/data/pathfinder/bernoulli-pathfinder.csv b/test/data/pathfinder/bernoulli-pathfinder.csv
index ad5ccff2..d034041e 100644
--- a/test/data/pathfinder/bernoulli-pathfinder.csv
+++ b/test/data/pathfinder/bernoulli-pathfinder.csv
@@ -1,1039 +1,1044 @@
 # stan_version_major = 2
-# stan_version_minor = 32
-# stan_version_patch = 2
+# stan_version_minor = 37
+# stan_version_patch = 0
 # model = bernoulli_model
-# start_datetime = 2023-08-25 18:20:52 UTC
+# start_datetime = 2026-04-12 00:35:48 UTC
 # method = pathfinder
 #   pathfinder
 #     init_alpha = 0.001 (Default)
-#     tol_obj = 9.9999999999999998e-13 (Default)
+#     tol_obj = 1e-12 (Default)
 #     tol_rel_obj = 10000 (Default)
 #     tol_grad = 1e-08 (Default)
-#     tol_rel_grad = 10000000 (Default)
+#     tol_rel_grad = 1e+07 (Default)
 #     tol_param = 1e-08 (Default)
 #     history_size = 5 (Default)
 #     num_psis_draws = 1000 (Default)
 #     num_paths = 4 (Default)
-#     save_single_paths = 0 (Default)
+#     save_single_paths = false (Default)
+#     psis_resample = true (Default)
+#     calculate_lp = true (Default)
 #     max_lbfgs_iters = 1000 (Default)
 #     num_draws = 1000 (Default)
 #     num_elbo_draws = 25 (Default)
 # id = 1 (Default)
 # data
-#   file = /home/brian/Dev/py/cmdstanpy/test/data/bernoulli.data.json
+#   file = test/data/bernoulli.data.json
 # init = 2 (Default)
 # random
-#   seed = 60096
+#   seed = 12345
 # output
-#   file = /tmp/tmpu_8pqsqm/bernoullix1z9yxm4/bernoulli-20230825142052.csv
+#   file = /tmp/tmpj7vksit8/bernoulli9hb1n4hj/bernoulli-20260411203548.csv
 #   diagnostic_file =  (Default)
 #   refresh = 100 (Default)
-#   sig_figs = -1 (Default)
+#   sig_figs = 8 (Default)
 #   profile_file = profile.csv (Default)
+#   save_cmdstan_config = true
 # num_threads = 1 (Default)
-lp_approx__,lp__,theta
--0.476551, -6.76206, 0.271327
--1.03168, -7.1987, 0.145419
--2.4285, -8.92516, 0.546197
--1.32207, -7.65466, 0.4371
--1.53739, -7.87901, 0.460369
--1.12454, -7.42633, 0.410357
--0.497944, -6.77806, 0.22023
--0.93635, -7.12896, 0.152721
--1.74264, -7.75439, 0.105291
--0.561756, -6.81822, 0.298664
--1.31969, -7.40828, 0.127362
--1.61855, -7.94818, 0.467076
--0.853618, -7.06293, 0.160499
--0.516076, -6.77093, 0.223904
--1.25333, -7.56654, 0.427208
--0.626345, -6.86948, 0.192008
--0.566813, -6.81689, 0.205629
--1.81711, -7.80861, 0.102414
--0.485188, -6.75037, 0.241505
--1.48451, -7.55049, 0.117398
--1.49785, -7.81761, 0.454244
--0.597054, -6.84943, 0.196709
--0.524347, -6.76562, 0.273927
--0.589162, -6.87127, 0.315193
--0.508892, -6.76518, 0.273615
--0.526366, -6.76768, 0.275313
--1.2809, -7.58501, 0.429321
--0.515714, -6.79968, 0.291537
--1.08516, -7.37888, 0.404289
--0.720885, -6.98905, 0.342663
--1.21042, -7.51517, 0.421205
--0.603394, -6.8608, 0.312244
--0.912615, -7.19824, 0.378964
--1.8676, -7.81361, 0.102155
--0.737547, -6.99206, 0.343271
--0.511134, -6.77588, 0.280252
--0.510004, -6.75108, 0.240313
--0.493452, -6.75001, 0.257941
--0.636148, -6.86832, 0.192268
--0.554036, -6.83687, 0.304983
--0.914616, -7.1106, 0.154789
--0.561853, -6.80424, 0.293389
--0.543132, -6.78486, 0.284908
--1.35137, -7.43078, 0.125683
--0.470003, -6.75434, 0.236132
--0.482583, -6.74804, 0.250683
--0.511373, -6.75251, 0.261965
--0.615103, -6.87287, 0.315632
--1.48451, -7.55049, 0.117398
--0.547459, -6.80618, 0.209068
--0.72476, -6.94711, 0.17715
--1.09627, -7.25885, 0.139717
--0.638048, -6.8846, 0.188752
--0.484429, -6.74986, 0.25763
--0.476269, -6.75965, 0.231266
--0.673947, -6.91074, 0.183582
--1.05347, -7.22239, 0.143114
--0.688123, -6.92693, 0.180622
--0.721566, -6.9443, 0.177619
--1.15416, -7.46034, 0.414585
--0.524232, -6.77841, 0.220062
--0.533364, -6.81676, 0.298138
--0.619038, -6.88407, 0.318644
--5.77772, -10.1268, 0.0384689
--0.543383, -6.79582, 0.212741
--0.595615, -6.83146, 0.201396
--1.52209, -7.57032, 0.116119
--1.11984, -7.41443, 0.408855
--0.48601, -6.77112, 0.277488
--0.500169, -6.76399, 0.22812
--0.564116, -6.84671, 0.308068
--0.93441, -7.21309, 0.381206
--0.59264, -6.82873, 0.202156
--4.27749, -9.21226, 0.0549588
--1.06407, -7.35554, 0.401227
--0.577253, -6.834, 0.304055
--0.535559, -6.80001, 0.291671
--0.983256, -7.16663, 0.148675
--0.483092, -6.74847, 0.246288
--0.937678, -7.22416, 0.382855
--0.48508, -6.75027, 0.241684
--1.5953, -7.95495, 0.467722
--0.788202, -7.00747, 0.167888
--0.911056, -7.19663, 0.378719
--2.8102, -9.26521, 0.568921
--0.463059, -6.74899, 0.255523
--1.7566, -8.14059, 0.484777
--0.598541, -6.85072, 0.196393
--0.985094, -7.2675, 0.389149
--0.616662, -6.86631, 0.19272
--0.868349, -7.14437, 0.370533
--2.75506, -8.45281, 0.0756527
--1.69102, -7.7004, 0.108287
--1.06603, -7.23048, 0.142345
--0.816516, -7.03524, 0.164075
--0.992746, -7.17345, 0.147969
--0.466377, -6.75228, 0.261646
--0.635039, -6.88203, 0.189289
--1.19915, -7.33221, 0.133367
--1.11937, -7.27746, 0.138048
--2.15059, -8.53127, 0.517193
--0.926578, -7.20155, 0.379467
--1.12208, -7.27618, 0.138161
--0.654639, -6.92051, 0.32772
--0.600609, -6.86346, 0.19337
--0.675617, -6.91218, 0.183313
--0.749676, -6.96888, 0.173633
--4.27749, -9.21226, 0.0549588
--0.586314, -6.85174, 0.196142
--1.3103, -7.41547, 0.126821
--0.954169, -7.14257, 0.151229
--0.534988, -6.77649, 0.280587
--0.644452, -6.89911, 0.185817
--1.06961, -7.23511, 0.14191
--0.557442, -6.81384, 0.29707
--1.09875, -7.39395, 0.406239
--0.729041, -6.95776, 0.175402
--0.740701, -7.0225, 0.349236
--0.641459, -6.87311, 0.191207
--0.696773, -6.93031, 0.180025
--0.49457, -6.75891, 0.231863
--0.635866, -6.90126, 0.323048
--0.644235, -6.87561, 0.190662
--0.550628, -6.78976, 0.215093
--3.69897, -8.9643, 0.0608185
--1.01412, -7.1856, 0.146729
--1.48817, -7.565, 0.116459
--1.7993, -7.76635, 0.104646
--0.698811, -6.9602, 0.336649
--0.686703, -6.92169, 0.181562
--3.31541, -8.76493, 0.0661085
--0.524232, -6.77841, 0.220062
--0.6204, -6.85407, 0.195579
--0.664773, -6.90727, 0.184239
--0.561638, -6.80006, 0.211191
--1.5482, -7.87199, 0.459677
--0.502082, -6.75798, 0.232638
--0.668035, -6.90565, 0.184548
--0.720358, -6.97333, 0.33943
--2.31182, -8.76251, 0.534606
--0.540925, -6.79716, 0.290478
--1.31708, -7.43372, 0.125468
--0.51659, -6.75776, 0.267709
--1.54662, -7.91801, 0.464175
--0.534151, -6.78745, 0.216041
--0.483741, -6.74905, 0.244357
--0.579635, -6.84625, 0.197503
--0.762851, -6.99336, 0.169923
--0.504648, -6.76037, 0.230708
--0.662141, -6.9104, 0.325298
--0.579328, -6.81647, 0.205757
--0.518412, -6.77307, 0.22274
--4.3007, -9.39725, 0.0510335
--0.48609, -6.75147, 0.260477
--0.482703, -6.74817, 0.252126
--0.655405, -6.9213, 0.327906
--0.484876, -6.75029, 0.25849
--2.50067, -8.2531, 0.0827528
--0.737547, -6.99206, 0.343271
--0.679143, -6.91521, 0.182749
--1.52767, -7.89565, 0.462001
--0.758427, -7.02826, 0.350331
--0.491842, -6.74844, 0.253629
--0.984875, -7.26927, 0.389401
--1.0793, -7.37456, 0.403727
--0.498706, -6.76372, 0.272576
--0.88251, -7.1672, 0.374166
--0.916666, -7.11215, 0.154611
--1.27527, -7.57901, 0.428637
--0.504361, -6.7607, 0.270251
--0.514673, -6.77936, 0.28213
--0.491842, -6.74844, 0.253629
--1.14149, -7.27974, 0.137847
--7.32737, -10.8822, 0.0290424
--0.482574, -6.74802, 0.250171
--2.82118, -9.45552, 0.58085
--0.55033, -6.78949, 0.215206
--0.546924, -6.8057, 0.209228
--1.85995, -8.24408, 0.493782
--1.44717, -7.76302, 0.448651
--2.97469, -8.59483, 0.0710988
--0.49457, -6.75891, 0.231863
--0.464446, -6.75038, 0.258657
--1.35756, -7.45043, 0.124251
--0.580172, -6.83698, 0.305019
--0.483292, -6.74865, 0.245604
--0.515714, -6.79968, 0.291537
--3.56948, -8.91715, 0.0620196
--0.53011, -6.7715, 0.277715
--0.592377, -6.857, 0.311141
--0.541998, -6.82515, 0.301093
--0.998687, -7.17406, 0.147906
--2.68293, -8.37102, 0.0784552
--1.27952, -7.37961, 0.129563
--0.536132, -6.77767, 0.281226
--0.490585, -6.77551, 0.280042
--0.46224, -6.74805, 0.250978
--1.33761, -7.44117, 0.124923
--0.911113, -7.18772, 0.377355
--0.50848, -6.74959, 0.24304
--0.613657, -6.86373, 0.193307
--0.515405, -6.79279, 0.213895
--0.636148, -6.86832, 0.192268
--1.14179, -7.29545, 0.136474
--1.38065, -7.71021, 0.443101
--2.66315, -8.39248, 0.0777063
--0.644452, -6.89911, 0.185817
--0.51127, -6.78931, 0.215276
--0.567676, -6.83209, 0.303428
--0.581574, -6.82484, 0.300987
--2.01155, -8.41724, 0.508156
--0.536151, -6.77768, 0.281237
--0.486847, -6.75188, 0.23913
--0.53082, -6.79531, 0.289686
--0.789836, -7.00367, 0.168428
--0.665532, -6.91404, 0.326179
--0.760107, -7.03002, 0.350663
--0.808746, -7.02456, 0.165513
--1.0461, -7.20943, 0.144365
--2.63301, -8.27172, 0.0820527
--1.05471, -7.34518, 0.399851
--3.9779, -9.12395, 0.0569619
--0.70141, -6.94479, 0.177537
--0.60206, -6.86465, 0.193098
--2.15609, -8.04921, 0.0910074
--0.604035, -6.84991, 0.196591
--0.53925, -6.77906, 0.219753
--0.932342, -7.20803, 0.380446
--1.28145, -7.5856, 0.429388
--1.00417, -7.1824, 0.147054
--1.98629, -7.86254, 0.0996736
--0.527551, -6.79207, 0.288263
--0.909871, -7.10563, 0.15536
--2.48582, -8.24339, 0.0831209
--1.1631, -7.46005, 0.414549
--0.506979, -6.74812, 0.251783
--7.32737, -10.8822, 0.0290424
--0.720159, -6.95969, 0.175091
--1.68952, -7.66497, 0.110331
--1.66739, -7.69907, 0.108363
--2.97469, -8.59483, 0.0710988
--0.654913, -6.90264, 0.323393
--2.40468, -8.21981, 0.0840255
--2.48615, -8.24361, 0.0831128
--0.515211, -6.75612, 0.234317
--1.45402, -7.52106, 0.119342
--0.503876, -6.78827, 0.286532
--1.53718, -7.58117, 0.115429
--1.12622, -7.26856, 0.138842
--1.56032, -7.57672, 0.115711
--1.33831, -7.65658, 0.437311
--0.676618, -6.92597, 0.329001
--0.860883, -7.14496, 0.370628
--0.483174, -6.74864, 0.254396
--0.977282, -7.15799, 0.14958
--1.52053, -7.86008, 0.458498
--0.659324, -6.94078, 0.332385
--0.718275, -6.98073, 0.340965
--1.0205, -7.30777, 0.394783
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+-1.3033393, -7.6325865, 4, 0.4346668
+-0.70359954, -6.9555314, 4, 0.33564066
+-0.85861861, -7.0625365, 4, 0.16054854
+-2.3537877, -8.8844949, 4, 0.54334631
+-0.62486579, -6.8710297, 4, 0.31512542
+-0.81350705, -7.0241461, 4, 0.16556936
+-0.64658079, -6.8780313, 4, 0.19013948
+-1.1498333, -7.3017006, 4, 0.13593502
+-0.7801996, -7.03911, 4, 0.35236403
+-0.80164564, -7.0139822, 4, 0.16697165
+-0.52926437, -6.7711722, 4, 0.27751726
+-0.52288891, -6.7646798, 4, 0.27326471
+-0.50639846, -6.748078, 4, 0.24867559
+-0.68790248, -6.9148736, 4, 0.18281056
+-2.0469324, -8.5138623, 4, 0.51583435
+-0.95673201, -7.2356092, 4, 0.38454277
+-1.5643018, -7.9382949, 4, 0.46612966
+-0.54197025, -6.7820772, 4, 0.21836108
+-0.55547367, -6.7981416, 4, 0.2908941
+-0.50833336, -6.7499666, 4, 0.24225865
+-1.0738563, -7.3683405, 4, 0.4029136
+-1.0738563, -7.3683405, 4, 0.4029136
+-0.59655516, -6.8410783, 4, 0.30632086
+-4.0005872, -9.2210515, 4, 0.054764023
+-0.64416312, -6.8758598, 4, 0.19060725
+-0.92067971, -7.1147047, 4, 0.15432048
+-0.69845376, -6.9242032, 4, 0.18110973
+-0.69845376, -6.9242032, 4, 0.18110973
+-0.51047617, -6.7520489, 4, 0.23889516
+-0.68066102, -6.9307495, 4, 0.33010668
+-0.69073896, -6.941622, 4, 0.33257458
+-0.53009993, -6.7708558, 4, 0.2239425
+-0.54509946, -6.7850202, 4, 0.21706737
+-0.50644349, -6.7481252, 4, 0.25180092
+-0.67512159, -6.9247837, 4, 0.32872383
+-3.460479, -10.235565, 4, 0.62490056
+-0.58578155, -6.8228137, 4, 0.2038523
+-3.3121129, -10.053821, 4, 0.61526015
+-0.77229617, -6.9887015, 4, 0.17061099
+-0.59369382, -6.8300771, 4, 0.20177834
+-0.51349528, -6.7551805, 4, 0.26515194
+-0.53223532, -6.7742081, 4, 0.27930617
+-1.0324278, -7.2069288, 4, 0.14460957
+-1.0324278, -7.2069288, 4, 0.14460957
+-0.57713965, -6.8148507, 4, 0.20625893
+-1.6490497, -7.6849166, 4, 0.10917261
+-0.64822447, -6.8959281, 4, 0.32170763
+-0.9631341, -7.2428207, 4, 0.38559632
+-0.73047143, -6.9523343, 4, 0.17628528
+-1.6118483, -7.9944767, 4, 0.47145836
+-0.815175, -7.0776459, 4, 0.35931888
+-1.3843408, -7.7269547, 4, 0.44487619
+-2.8907091, -9.5382294, 4, 0.58587477
+-0.60919317, -6.844234, 4, 0.19801293
+-0.60919317, -6.844234, 4, 0.19801293
+-0.53125899, -6.7732097, 4, 0.27872942
+-2.5600368, -9.134997, 4, 0.56044451
+-0.55212042, -6.7946697, 4, 0.28940997
+-0.65693058, -6.8873067, 4, 0.18819071
+-0.60446318, -6.8399233, 4, 0.19912442
+-0.58824547, -6.8250783, 4, 0.2031941
+-1.0502196, -7.3414239, 4, 0.39934824
+-0.77064521, -7.0286215, 4, 0.35039913
+-0.54334235, -6.7856089, 4, 0.28526878
+-0.53064334, -6.7713717, 4, 0.22365748
+-0.81918938, -7.0820826, 4, 0.36009508
+-0.61124798, -6.846104, 4, 0.19753948
+-1.0677785, -7.2356817, 4, 0.14185572
+-0.51202855, -6.7537062, 4, 0.26348336
+-2.2592646, -8.7700308, 4, 0.53515303
+-2.2592646, -8.7700308, 4, 0.53515303
+-0.50830842, -6.7499819, 4, 0.25788052
+-0.53922745, -6.7794927, 4, 0.21954746
+-3.2702083, -8.7798394, 4, 0.06569303
+-0.68197055, -6.9096151, 4, 0.18379411
+-2.1068741, -8.5860315, 4, 0.52142354
+-0.60127634, -6.8460546, 4, 0.30786639
+-2.0238322, -7.9556396, 4, 0.095202719
+-1.3168615, -7.6483042, 4, 0.43640238
+-0.77714995, -6.9928956, 4, 0.16999135
+-0.53207289, -6.7727277, 4, 0.22292374
+-0.74886174, -7.0047736, 4, 0.34580197
+-0.68664977, -6.9372075, 4, 0.33158043
+-0.68664977, -6.9372075, 4, 0.33158043
+-0.96369423, -7.1504531, 4, 0.1503812
+-0.88535087, -7.1555819, 4, 0.37232859
+-0.59562803, -6.8318488, 4, 0.20128814
+-0.64449239, -6.8919396, 4, 0.32068979
+-0.64449239, -6.8919396, 4, 0.32068979
+-1.0000193, -7.2844715, 4, 0.39154726
+-0.61406352, -6.848664, 4, 0.19689957
+-0.57682742, -6.8145624, 4, 0.20634897
+-0.57682742, -6.8145624, 4, 0.20634897
+-0.75972127, -7.0166508, 4, 0.34811492
+-0.67519234, -6.9035943, 4, 0.1849437
+-0.53387314, -6.7758843, 4, 0.28025137
+-0.84786327, -7.0534208, 4, 0.16170383
+-0.59578587, -6.8319933, 4, 0.20124841
+-1.2508468, -7.3816775, 4, 0.12940132
+-0.50667124, -6.7483516, 4, 0.25321999
+-0.87364491, -7.0752341, 4, 0.15897449
+-0.89814312, -7.0958436, 4, 0.15650148
+-0.89814312, -7.0958436, 4, 0.15650148
+-0.55644886, -6.7956468, 4, 0.21280642
+-0.85890036, -7.126115, 4, 0.36755663
+-0.50704107, -6.748706, 4, 0.24539484
+-2.5743561, -9.1524215, 4, 0.56159429
+-0.95289871, -7.1415114, 4, 0.15134405
+-1.8816724, -7.8544442, 4, 0.10007783
+-0.51999717, -6.7612219, 4, 0.23006571
+-0.51224084, -6.7539194, 4, 0.26373676
+-0.53433597, -6.7763583, 4, 0.28051369
+-2.4493603, -8.2488568, 4, 0.082913376
+-0.51731864, -6.7590359, 4, 0.26885091
+-1.3453494, -7.4553005, 4, 0.12390117
+-1.723994, -7.7400937, 4, 0.10607135
+-0.6969236, -6.9483062, 4, 0.33406021
+-0.6969236, -6.9483062, 4, 0.33406021
+-0.52463898, -6.7656591, 4, 0.22702722
+-2.0838232, -7.9978321, 4, 0.093275989
+-2.0838232, -7.9978321, 4, 0.093275989
+-1.5383495, -7.6023794, 4, 0.11410133
+-0.72729666, -6.9495565, 4, 0.17674315
+-3.0925252, -8.6683298, 4, 0.068884206
+-0.89956313, -7.1714582, 4, 0.37483393
+-0.52804407, -6.7689022, 4, 0.22505392
+-0.52804407, -6.7689022, 4, 0.22505392
+-0.96311287, -7.2427968, 4, 0.38559283
+-0.51001294, -6.7516854, 4, 0.26080035
+-0.53736094, -6.7794597, 4, 0.28218074
+-0.53259849, -6.7745796, 4, 0.27951812
+-1.5240908, -7.8908876, 4, 0.46153504
+-0.53195767, -6.7739241, 4, 0.2791432
+-0.53280034, -6.7734172, 4, 0.22255883
+-0.92933424, -7.2048091, 4, 0.37995995
+-3.7940996, -9.0989456, 4, 0.057545528
+-3.7940996, -9.0989456, 4, 0.057545528
+-0.81551823, -7.0258666, 4, 0.16533522
+-0.81551823, -7.0258666, 4, 0.16533522
+-0.50651655, -6.7481933, 4, 0.24768917
+-0.79016906, -7.0041192, 4, 0.16836481
+-3.9816192, -9.2099162, 4, 0.055010734
+-0.80073982, -7.0132047, 4, 0.16708028
+-0.62115825, -6.8670926, 4, 0.31402996
+-0.52820326, -6.7690536, 4, 0.22496589
+-0.50713246, -6.7488103, 4, 0.25498583
+-0.50634824, -6.7480301, 4, 0.25051065
+-0.50664996, -6.7483237, 4, 0.24693636
+-1.662508, -7.6948659, 4, 0.1086025
+-0.60656565, -6.8418404, 4, 0.19862655
+-0.65289302, -6.8836922, 4, 0.1889409
+-1.3449634, -7.6810155, 4, 0.43996837
+-3.753751, -10.594851, 4, 0.64299242
+-0.69009608, -6.9409277, 4, 0.33241892
+-0.50657759, -6.748253, 4, 0.24731824
+-0.66993483, -6.9192048, 4, 0.32741105
+-0.8856233, -7.1558859, 4, 0.37237699
+-1.3557039, -7.4633003, 4, 0.12332973
+-4.4894074, -9.502626, 4, 0.048948832
+-0.90202381, -7.17421, 4, 0.37526376
+-0.51600852, -6.7577129, 4, 0.26766562
+-0.57515161, -6.8186215, 4, 0.29880711
+-2.5995839, -8.3492157, 4, 0.07922609
+-0.83636196, -7.0436474, 4, 0.16296717
+-0.53573088, -6.7761909, 4, 0.22114136
+-0.53950666, -6.7816631, 4, 0.28331783
+-1.7843578, -8.1993616, 4, 0.48993152
+-0.52913733, -6.7710425, 4, 0.27743842
+-0.7588018, -6.977013, 4, 0.17237378
+-1.6352777, -8.0222096, 4, 0.47404444
+-0.50876097, -6.7503828, 4, 0.24147651
+-0.93792307, -7.2144536, 4, 0.38140989
+-1.248441, -7.3797886, 4, 0.12954853
+-0.56289344, -6.8016505, 4, 0.21062554
+-0.66126057, -6.9098898, 4, 0.32517441
+-0.50750275, -6.7491789, 4, 0.2560452
+-0.50750275, -6.7491789, 4, 0.2560452
+-0.72592977, -6.9797708, 4, 0.34076652
+-0.79967606, -7.0605424, 4, 0.35628081
+-1.2606233, -7.3893462, 4, 0.12880699
+-1.2606233, -7.3893462, 4, 0.12880699
+-1.2606233, -7.3893462, 4, 0.12880699
+-0.51497944, -6.7564014, 4, 0.23405527
+-2.1541937, -8.643089, 4, 0.52575864
+-0.73251101, -6.9541174, 4, 0.17599333
+-1.3217776, -7.4370405, 4, 0.12522399
+-1.6348867, -8.0217464, 4, 0.47400148
+-2.6095867, -8.3558438, 4, 0.078990628
+-1.5683655, -7.9430914, 4, 0.46658943
+-1.5683655, -7.9430914, 4, 0.46658943
+-0.50639121, -6.7480731, 4, 0.25126847
+-0.98854185, -7.2714928, 4, 0.38971657
+-0.51392026, -6.7553802, 4, 0.23504416
+-1.2123987, -7.351398, 4, 0.13180077
+-1.2123987, -7.351398, 4, 0.13180077
+-3.2833216, -10.01856, 4, 0.61334908
+-0.50636266, -6.7480432, 4, 0.24918196
+-0.50829354, -6.7499671, 4, 0.25785041
+-1.4220296, -7.7710319, 4, 0.44948126
+-0.55358209, -6.7929693, 4, 0.21382715
+-1.5716111, -7.6273124, 4, 0.11257354
+-0.94552275, -7.2229958, 4, 0.38268261
+-0.54146502, -6.7836766, 4, 0.28432623
+-1.8087802, -8.2284885, 4, 0.49244665
+-3.7091321, -9.0481203, 4, 0.058754592
+-0.61022967, -6.8555125, 4, 0.31070644
+-0.53967114, -6.7818322, 4, 0.28340357
+-0.53789107, -6.7800038, 4, 0.28246498
+-0.53789107, -6.7800038, 4, 0.28246498
+-1.5314268, -7.5971754, 4, 0.11442468
+-1.741776, -7.7531054, 4, 0.10536087
+-0.85812805, -7.1252562, 4, 0.36741504
+-1.2097263, -7.3492862, 4, 0.13197136
+-0.51468289, -6.7561156, 4, 0.23432544
+-1.4400694, -7.7921654, 4, 0.45165497
+-2.5691948, -8.3290379, 4, 0.079948882
+-2.5691948, -8.3290379, 4, 0.079948882
+-1.7009119, -7.7231582, 4, 0.10700776
+-0.50634533, -6.7480265, 4, 0.24961588
+-0.50634533, -6.7480265, 4, 0.24961588
+-0.82186269, -7.0312882, 4, 0.16460327
 # 
-# Elapsed Time: 0.001000 seconds (Pathfinders)
-#               0.001000 seconds (PSIS)
-#               0.002000 seconds (Total)
+# Elapsed Time: 0.000000 seconds (Pathfinders)
+#               0.000000 seconds (PSIS)
+#               0.000000 seconds (Total)
 # 
diff --git a/test/data/pathfinder/bernoulli-pathfinder_config.json b/test/data/pathfinder/bernoulli-pathfinder_config.json
new file mode 100644
index 00000000..c8555515
--- /dev/null
+++ b/test/data/pathfinder/bernoulli-pathfinder_config.json
@@ -0,0 +1,48 @@
+
+{
+  "stan_major_version" : "2",
+  "stan_minor_version" : "37",
+  "stan_patch_version" : "0",
+  "model_name" : "bernoulli_model",
+  "start_datetime" : "2026-04-12 00:35:48 UTC",
+  "method" : {
+    "value" : "pathfinder",
+    "pathfinder" : {
+      "init_alpha" : 0.001,
+      "tol_obj" : 1e-12,
+      "tol_rel_obj" : 10000,
+      "tol_grad" : 1e-08,
+      "tol_rel_grad" : 10000000,
+      "tol_param" : 1e-08,
+      "history_size" : 5,
+      "num_psis_draws" : 1000,
+      "num_paths" : 4,
+      "save_single_paths" : false,
+      "psis_resample" : true,
+      "calculate_lp" : true,
+      "max_lbfgs_iters" : 1000,
+      "num_draws" : 1000,
+      "num_elbo_draws" : 25
+    }
+  },
+  "id" : 1,
+  "data" : {
+    "file" : "test\/data\/bernoulli.data.json"
+  },
+  "init" : "2",
+  "random" : {
+    "seed" : 12345
+  },
+  "output" : {
+    "file" : "\/tmp\/tmpj7vksit8\/bernoulli9hb1n4hj\/bernoulli-20260411203548.csv",
+    "diagnostic_file" : "",
+    "refresh" : 100,
+    "sig_figs" : 8,
+    "profile_file" : "profile.csv",
+    "save_cmdstan_config" : true
+  },
+  "num_threads" : 1,
+  "mpi_enabled" : false,
+  "stanc_version" : "stanc3 v2.37.0",
+  "stancflags" : "--filename-in-msg=bernoulli.stan"
+}
diff --git a/test/test_metadata.py b/test/test_metadata.py
index e451c762..bf45849f 100644
--- a/test/test_metadata.py
+++ b/test/test_metadata.py
@@ -5,13 +5,24 @@
 import os
 import tempfile
 from pathlib import Path
+from typing import Any
 
 import pytest
 from pydantic import ValidationError
 
 from cmdstanpy.cmdstan_args import CmdStanArgs, SamplerArgs
 from cmdstanpy.stanfit import InferenceMetadata, RunSet
-from cmdstanpy.stanfit.metadata import MetricInfo
+from cmdstanpy.stanfit.metadata import (
+    GeneratedQuantitiesConfig,
+    LaplaceConfig,
+    MetricInfo,
+    OptimizeConfig,
+    PathfinderConfig,
+    SampleConfig,
+    StanConfig,
+    VariationalConfig,
+    parse_config,
+)
 from cmdstanpy.utils import EXTENSION, check_sampler_csv
 
 HERE = os.path.dirname(os.path.abspath(__file__))
@@ -22,6 +33,213 @@
 BADFILES_PATH = os.path.join(DATAFILES_PATH, 'runset-bad')
 
 
+def make_config_output(
+    method_name: str, method_body: dict[str, Any]
+) -> dict[str, Any]:
+    return {
+        'stan_major_version': '2',
+        'stan_minor_version': '37',
+        'stan_patch_version': '0',
+        'model_name': 'mu_model',
+        'start_datetime': '2026-01-23 22:20:01 UTC',
+        'method': {
+            'value': method_name,
+            method_name: method_body,
+        },
+        'id': 1,
+        'data': {'file': ''},
+        'init': '2',
+        'random': {'seed': 12345},
+        'output': {
+            'file': '/tmp/mu.csv',
+            'diagnostic_file': '',
+            'refresh': 100,
+            'sig_figs': 8,
+            'profile_file': 'profile.csv',
+            'save_cmdstan_config': True,
+        },
+        'num_threads': 4,
+        'mpi_enabled': False,
+        'stanc_version': 'stanc3 v2.37.0',
+        'stancflags': '--filename-in-msg=mu.stan',
+    }
+
+
+CONFIG_OUTPUT_CASES: list[
+    tuple[str, dict[str, Any], type[Any], dict[str, Any]]
+] = [
+    (
+        'sample',
+        make_config_output(
+            'sample',
+            {
+                'num_samples': 1000,
+                'num_warmup': 1000,
+                'save_warmup': False,
+                'thin': 1,
+                'adapt': {
+                    'engaged': True,
+                    'gamma': 0.05,
+                    'delta': 0.8,
+                    'kappa': 0.75,
+                    't0': 10,
+                    'init_buffer': 75,
+                    'term_buffer': 50,
+                    'window': 25,
+                    'save_metric': True,
+                },
+                'algorithm': {
+                    'value': 'hmc',
+                    'hmc': {
+                        'engine': {
+                            'value': 'nuts',
+                            'nuts': {'max_depth': 10},
+                        },
+                        'metric': {'value': 'diag_e'},
+                        'metric_file': '',
+                        'stepsize': 1,
+                        'stepsize_jitter': 0,
+                    },
+                },
+                'num_chains': 4,
+            },
+        ),
+        SampleConfig,
+        {
+            'algorithm': 'hmc',
+            'num_samples': 1000,
+            'num_warmup': 1000,
+            'save_warmup': False,
+            'thin': 1,
+            'max_depth': 10,
+        },
+    ),
+    (
+        'optimize',
+        make_config_output(
+            'optimize',
+            {
+                'algorithm': {
+                    'value': 'lbfgs',
+                    'lbfgs': {
+                        'init_alpha': 0.001,
+                        'tol_obj': 1e-12,
+                        'tol_rel_obj': 10000,
+                        'tol_grad': 1e-08,
+                        'tol_rel_grad': 10000000,
+                        'tol_param': 1e-08,
+                        'history_size': 5,
+                    },
+                },
+                'jacobian': False,
+                'iter': 2000,
+                'save_iterations': False,
+            },
+        ),
+        OptimizeConfig,
+        {
+            'algorithm': 'lbfgs',
+            'jacobian': False,
+            'save_iterations': False,
+        },
+    ),
+    (
+        'variational',
+        make_config_output(
+            'variational',
+            {
+                'algorithm': {
+                    'value': 'meanfield',
+                    'meanfield': {},
+                },
+                'iter': 10000,
+                'grad_samples': 1,
+                'elbo_samples': 100,
+                'eta': 1,
+                'adapt': {
+                    'engaged': True,
+                    'iter': 50,
+                },
+                'tol_rel_obj': 0.01,
+                'eval_elbo': 100,
+                'output_samples': 1000,
+            },
+        ),
+        VariationalConfig,
+        {
+            'algorithm': 'meanfield',
+            'iter': 10000,
+            'grad_samples': 1,
+            'elbo_samples': 100,
+            'eta': 1.0,
+        },
+    ),
+    (
+        'pathfinder',
+        make_config_output(
+            'pathfinder',
+            {
+                'init_alpha': 0.001,
+                'tol_obj': 1e-12,
+                'tol_rel_obj': 10000,
+                'tol_grad': 1e-08,
+                'tol_rel_grad': 10000000,
+                'tol_param': 1e-08,
+                'history_size': 5,
+                'num_psis_draws': 1000,
+                'num_paths': 4,
+                'save_single_paths': False,
+                'psis_resample': True,
+                'calculate_lp': True,
+                'max_lbfgs_iters': 1000,
+                'num_draws': 1000,
+                'num_elbo_draws': 25,
+            },
+        ),
+        PathfinderConfig,
+        {
+            'num_draws': 1000,
+            'num_paths': 4,
+            'psis_resample': True,
+            'calculate_lp': True,
+        },
+    ),
+    (
+        'laplace',
+        make_config_output(
+            'laplace',
+            {
+                'mode': '/tmp/mu-opt.csv',
+                'jacobian': True,
+                'draws': 1000,
+                'calculate_lp': True,
+            },
+        ),
+        LaplaceConfig,
+        {
+            'mode': '/tmp/mu-opt.csv',
+            'draws': 1000,
+            'jacobian': True,
+        },
+    ),
+    (
+        'generate_quantities',
+        make_config_output(
+            'generate_quantities',
+            {
+                'fitted_params': '/tmp/mu-fit.csv',
+                'num_chains': 1,
+            },
+        ),
+        GeneratedQuantitiesConfig,
+        {
+            'fitted_params': '/tmp/mu-fit.csv',
+            'num_chains': 1,
+        },
+    ),
+]
+
+
 def test_good() -> None:
     # construct fit using existing sampler output
     exe = os.path.join(DATAFILES_PATH, 'bernoulli' + EXTENSION)
@@ -77,6 +295,46 @@ def test_good() -> None:
     assert bern_model_vars == metadata.stan_vars.keys()
 
 
+@pytest.mark.parametrize(
+    'method_name, config_json, expected_type, expected_fields',
+    CONFIG_OUTPUT_CASES,
+)
+def test_parse_config_outputs(
+    method_name: str,
+    config_json: dict[str, Any],
+    expected_type: type[Any],
+    expected_fields: dict[str, Any],
+) -> None:
+    parsed = parse_config(json.dumps(config_json))
+
+    assert isinstance(parsed, StanConfig)
+    assert parsed.model_name == 'mu_model'
+    assert parsed.stan_major_version == '2'
+    assert parsed.stan_minor_version == '37'
+    assert parsed.stan_patch_version == '0'
+    assert isinstance(parsed.method_config, expected_type)
+    assert parsed.method_config.method == method_name
+
+    dumped = parsed.method_config.model_dump()
+    for key, expected in expected_fields.items():
+        assert dumped[key] == expected
+
+
+def test_parse_config_accepts_bytes() -> None:
+    config_json = make_config_output(
+        'generate_quantities',
+        {
+            'fitted_params': '/tmp/mu-fit.csv',
+            'num_chains': 1,
+        },
+    )
+
+    parsed = parse_config(json.dumps(config_json).encode())
+
+    assert isinstance(parsed.method_config, GeneratedQuantitiesConfig)
+    assert parsed.method_config.fitted_params == '/tmp/mu-fit.csv'
+
+
 class TestMetricInfoValidators:
     """Test custom validators for MetricInfo model"""
 

From ca13a2eee6f55eddbb6d5cb2fc81cec55fd96fd7 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Wed, 15 Apr 2026 20:50:50 -0400
Subject: [PATCH 10/17] Fixup Path conversion to string

---
 cmdstanpy/stanfit/__init__.py   |  2 +-
 cmdstanpy/stanfit/pathfinder.py | 10 ++++++----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/cmdstanpy/stanfit/__init__.py b/cmdstanpy/stanfit/__init__.py
index 787d8238..6dd3413d 100644
--- a/cmdstanpy/stanfit/__init__.py
+++ b/cmdstanpy/stanfit/__init__.py
@@ -92,7 +92,7 @@ def from_csv(
                 if os.path.splitext(file)[1] == ".csv":
                     csvfiles.append(os.path.join(path, file))
         elif os.path.exists(path):
-            csvfiles.append(str(path))
+            csvfiles.append(os.fspath(path))
         else:
             raise ValueError('Invalid path specification: {}'.format(path))
     else:
diff --git a/cmdstanpy/stanfit/pathfinder.py b/cmdstanpy/stanfit/pathfinder.py
index bc0339d6..49c743f5 100644
--- a/cmdstanpy/stanfit/pathfinder.py
+++ b/cmdstanpy/stanfit/pathfinder.py
@@ -53,11 +53,13 @@ def from_files(
         metadata = InferenceMetadata.from_csv(csv_file)
         return cls(
             metadata=metadata,
-            csv_file=str(csv_file),
+            csv_file=os.fspath(csv_file),
             model_name=stan_config.model_name,
             config=stan_config.method_config,
-            config_file=str(config_file),
-            stdout_file=str(stdout_file) if stdout_file is not None else None,
+            config_file=os.fspath(config_file),
+            stdout_file=(
+                os.fspath(stdout_file) if stdout_file is not None else None
+            ),
         )
 
     def create_inits(
@@ -255,4 +257,4 @@ def save_csvfiles(self, dir: str | None = None) -> None:
             if dst.exists():
                 raise ValueError(f'File exists, not overwriting: {dst}')
             shutil.move(src, dst)
-            setattr(self, attr, str(dst))
+            setattr(self, attr, os.fspath(dst))

From c5a42db7458710363489df422f3da01f8ebf3559 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Wed, 15 Apr 2026 21:32:09 -0400
Subject: [PATCH 11/17] Convert StanConfig to Generic for richer typing

---
 cmdstanpy/stanfit/metadata.py   | 37 ++++++++++++++++++++-------------
 cmdstanpy/stanfit/pathfinder.py | 19 +++++++----------
 2 files changed, 30 insertions(+), 26 deletions(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index 1dac3b1d..051cd2a1 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -6,7 +6,7 @@
 import json
 import math
 import os
-from typing import Annotated, Any, Iterator, Literal
+from typing import Annotated, Any, Generic, Iterator, Literal
 
 import stanio
 from pydantic import (
@@ -16,6 +16,7 @@
     field_validator,
     model_validator,
 )
+from typing_extensions import TypeVar
 
 from cmdstanpy.utils import stancsv
 
@@ -196,7 +197,20 @@ class GeneratedQuantitiesConfig(BaseModel):
     num_chains: int = 1
 
 
-class StanConfig(BaseModel):
+AnyMethodConfig = Annotated[
+    SampleConfig
+    | OptimizeConfig
+    | PathfinderConfig
+    | LaplaceConfig
+    | VariationalConfig
+    | GeneratedQuantitiesConfig,
+    Discriminator("method"),
+]
+
+MethodT = TypeVar("MethodT", bound=BaseModel, default=AnyMethodConfig)
+
+
+class StanConfig(BaseModel, Generic[MethodT]):
     """Common representation of a config JSON file output as part of a
     Stan inference run. Separate method-specific config classes handle
     the variation of output between methods."""
@@ -208,15 +222,7 @@ class StanConfig(BaseModel):
     stan_minor_version: str
     stan_patch_version: str
 
-    method_config: Annotated[
-        SampleConfig
-        | OptimizeConfig
-        | PathfinderConfig
-        | LaplaceConfig
-        | VariationalConfig
-        | GeneratedQuantitiesConfig,
-        Discriminator("method"),
-    ]
+    method_config: MethodT
 
 
 def flatten_value_dict(data: dict[str, Any]) -> dict[str, Any]:
@@ -280,9 +286,12 @@ def flatten_config(data: dict[str, Any]) -> dict[str, Any]:
     return result
 
 
-def parse_config(json_data: str | bytes) -> StanConfig:
-    """Parse a CmdStan config JSON string into a StanConfig."""
+def parse_config(
+    json_data: str | bytes, method_type: type[MethodT]
+) -> StanConfig[MethodT]:
+    """Parse a CmdStan config JSON string into a StanConfig with the
+    given method type."""
 
     raw = json.loads(json_data)
     flat = flatten_config(raw)
-    return StanConfig.model_validate(flat)  # type: ignore
+    return StanConfig[method_type].model_validate(flat)  # type: ignore
diff --git a/cmdstanpy/stanfit/pathfinder.py b/cmdstanpy/stanfit/pathfinder.py
index 49c743f5..0e02590a 100644
--- a/cmdstanpy/stanfit/pathfinder.py
+++ b/cmdstanpy/stanfit/pathfinder.py
@@ -14,6 +14,7 @@
 from cmdstanpy.stanfit.metadata import (
     InferenceMetadata,
     PathfinderConfig,
+    StanConfig,
     parse_config,
 )
 from cmdstanpy.utils import stancsv
@@ -29,7 +30,7 @@ class CmdStanPathfinder:
     metadata: InferenceMetadata
     model_name: str
     csv_file: str
-    config: PathfinderConfig
+    config: StanConfig[PathfinderConfig]
     config_file: str | None = None
     stdout_file: str | None = None
     _draws: np.ndarray = field(default_factory=lambda: np.array(()), init=False)
@@ -42,20 +43,14 @@ def from_files(
         stdout_file: str | os.PathLike | None = None,
     ) -> CmdStanPathfinder:
         with open(config_file) as f:
-            stan_config = parse_config(f.read())
-
-        if not isinstance(stan_config.method_config, PathfinderConfig):
-            conf_name = type(stan_config.method_config).__name__
-            raise ValueError(
-                f"Did not find Pathfinder config, instead found: {conf_name}"
-            )
+            stan_config = parse_config(f.read(), PathfinderConfig)
 
         metadata = InferenceMetadata.from_csv(csv_file)
         return cls(
             metadata=metadata,
             csv_file=os.fspath(csv_file),
             model_name=stan_config.model_name,
-            config=stan_config.method_config,
+            config=stan_config,
             config_file=os.fspath(config_file),
             stdout_file=(
                 os.fspath(stdout_file) if stdout_file is not None else None
@@ -229,9 +224,9 @@ def is_resampled(self) -> bool:
         approximations, False otherwise.
         """
         return (
-            self.config.num_paths > 1
-            and self.config.psis_resample
-            and self.config.calculate_lp
+            self.config.method_config.num_paths > 1
+            and self.config.method_config.psis_resample
+            and self.config.method_config.calculate_lp
         )
 
     def save_csvfiles(self, dir: str | None = None) -> None:

From 610ad2fcf59b6024ba8444ad43876809a70881ec Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Wed, 15 Apr 2026 21:43:18 -0400
Subject: [PATCH 12/17] Minor comment on config_file optionality

---
 cmdstanpy/stanfit/pathfinder.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmdstanpy/stanfit/pathfinder.py b/cmdstanpy/stanfit/pathfinder.py
index 0e02590a..fa08f272 100644
--- a/cmdstanpy/stanfit/pathfinder.py
+++ b/cmdstanpy/stanfit/pathfinder.py
@@ -31,7 +31,7 @@ class CmdStanPathfinder:
     model_name: str
     csv_file: str
     config: StanConfig[PathfinderConfig]
-    config_file: str | None = None
+    config_file: str | None = None  # None if config object passed directly
     stdout_file: str | None = None
     _draws: np.ndarray = field(default_factory=lambda: np.array(()), init=False)
 

From 1e2f272b3e1864bd9891c9c5c0a42308e6c22ad4 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Wed, 15 Apr 2026 22:04:35 -0400
Subject: [PATCH 13/17] Refactor CmdStanLaplace

---
 cmdstanpy/model.py            |   8 +-
 cmdstanpy/stanfit/__init__.py |  35 +++-----
 cmdstanpy/stanfit/laplace.py  | 156 ++++++++++++++++++++--------------
 test/test_laplace.py          |   2 +-
 4 files changed, 112 insertions(+), 89 deletions(-)

diff --git a/cmdstanpy/model.py b/cmdstanpy/model.py
index 0a688a18..ecc277ce 100644
--- a/cmdstanpy/model.py
+++ b/cmdstanpy/model.py
@@ -1040,7 +1040,7 @@ def generate_quantities(
             ),
         ):
             fit_object = previous_fit
-            if isinstance(previous_fit, CmdStanPathfinder):
+            if isinstance(previous_fit, (CmdStanPathfinder, CmdStanLaplace)):
                 fit_csv_files = [previous_fit.csv_file]
             else:
                 fit_csv_files = previous_fit.runset.csv_files
@@ -1787,7 +1787,11 @@ def laplace_sample(
                 timeout=timeout,
             )
         runset.raise_for_timeouts()
-        return CmdStanLaplace(runset, cmdstan_mode)
+        return CmdStanLaplace.from_files(
+            csv_file=runset.csv_files[0],
+            config_file=runset.config_files[0],
+            stdout_file=runset.stdout_files[0],
+        )
 
     def _run_cmdstan(
         self,
diff --git a/cmdstanpy/stanfit/__init__.py b/cmdstanpy/stanfit/__init__.py
index 6dd3413d..1729aec7 100644
--- a/cmdstanpy/stanfit/__init__.py
+++ b/cmdstanpy/stanfit/__init__.py
@@ -5,7 +5,6 @@
 
 from cmdstanpy.cmdstan_args import (
     CmdStanArgs,
-    LaplaceArgs,
     OptimizeArgs,
     SamplerArgs,
     VariationalArgs,
@@ -233,27 +232,21 @@ def from_csv(
                 runset._set_retcode(i, 0)
             return CmdStanVB(runset)
         elif config_dict['method'] == 'laplace':
-            jacobian = config_dict['jacobian'] == 1
-            laplace_args = LaplaceArgs(
-                mode=config_dict['mode'],  # type: ignore
-                draws=config_dict['draws'],  # type: ignore
-                jacobian=jacobian,
-            )
-            cmdstan_args = CmdStanArgs(
-                model_name=model,
-                model_exe=model,
-                chain_ids=None,
-                method_args=laplace_args,
-            )
-            runset = RunSet(args=cmdstan_args)
-            runset._csv_files = csvfiles
-            for i in range(len(runset._retcodes)):
-                runset._set_retcode(i, 0)
-            mode: CmdStanMLE = from_csv(
-                config_dict['mode'],  # type: ignore
-                method='optimize',
+            if len(csvfiles) != 1:
+                raise ValueError(
+                    'Expecting a single Laplace Stan CSV file, '
+                    f'found {len(csvfiles)}'
+                )
+            csv_file = csvfiles[0]
+            config_file = os.path.splitext(csv_file)[0] + '_config.json'
+            if not os.path.exists(config_file):
+                raise ValueError(
+                    'Laplace config file not found at expected path: '
+                    f'{config_file}'
+                )
+            return CmdStanLaplace.from_files(
+                csv_file=csv_file, config_file=config_file
             )
-            return CmdStanLaplace(runset, mode=mode)
         elif config_dict['method'] == 'pathfinder':
             if len(csvfiles) != 1:
                 raise ValueError(
diff --git a/cmdstanpy/stanfit/laplace.py b/cmdstanpy/stanfit/laplace.py
index bab78c3f..c1277133 100644
--- a/cmdstanpy/stanfit/laplace.py
+++ b/cmdstanpy/stanfit/laplace.py
@@ -4,7 +4,12 @@
 
 from __future__ import annotations
 
-from typing import Any, Hashable, MutableMapping
+import os
+import shutil
+from collections.abc import Hashable
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, MutableMapping
 
 import numpy as np
 import pandas as pd
@@ -16,30 +21,60 @@
 except ImportError:
     XARRAY_INSTALLED = False
 
-from cmdstanpy.cmdstan_args import Method
 from cmdstanpy.utils import stancsv
 from cmdstanpy.utils.data_munging import build_xarray_data
 
-from .metadata import InferenceMetadata
+from .metadata import InferenceMetadata, LaplaceConfig, StanConfig, parse_config
 from .mle import CmdStanMLE
-from .runset import RunSet
 
 # TODO list:
 # - docs and example notebook
 
 
+@dataclass
 class CmdStanLaplace:
-    def __init__(self, runset: RunSet, mode: CmdStanMLE) -> None:
-        """Initialize object."""
-        if not runset.method == Method.LAPLACE:
-            raise ValueError(
-                'Wrong runset method, expecting laplace runset, '
-                'found method {}'.format(runset.method)
-            )
-        self.runset = runset
-        self._mode = mode
-        self._draws: np.ndarray = np.array(())
-        self._metadata = InferenceMetadata.from_csv(self.runset.csv_files[0])
+    """
+    Container for outputs from the Laplace approximation.
+    Created by :meth:`CmdStanModel.laplace_sample`.
+    """
+
+    metadata: InferenceMetadata
+    model_name: str
+    csv_file: str
+    config: StanConfig[LaplaceConfig]
+    mode: CmdStanMLE
+    config_file: str | None = None  # None if config object passed directly
+    stdout_file: str | None = None
+    _draws: np.ndarray = field(default_factory=lambda: np.array(()), init=False)
+
+    @classmethod
+    def from_files(
+        cls,
+        csv_file: str | os.PathLike,
+        config_file: str | os.PathLike,
+        stdout_file: str | os.PathLike | None = None,
+    ) -> CmdStanLaplace:
+        # Local import to avoid circular dependency with stanfit.__init__
+        from cmdstanpy.stanfit import from_csv
+
+        with open(config_file) as f:
+            stan_config = parse_config(f.read(), LaplaceConfig)
+
+        metadata = InferenceMetadata.from_csv(csv_file)
+        # temporary - should have done CmdStanMLE first
+        mode = from_csv(stan_config.method_config.mode, method='optimize')
+        assert isinstance(mode, CmdStanMLE)
+        return cls(
+            metadata=metadata,
+            csv_file=os.fspath(csv_file),
+            model_name=stan_config.model_name,
+            config=stan_config,
+            mode=mode,
+            config_file=os.fspath(config_file),
+            stdout_file=(
+                os.fspath(stdout_file) if stdout_file is not None else None
+            ),
+        )
 
     def create_inits(
         self, seed: int | None = None, chains: int = 4
@@ -63,13 +98,13 @@ def create_inits(
             draw = self._draws[idxs[0]]
             return {
                 name: var.extract_reshape(draw)
-                for name, var in self._metadata.stan_vars.items()
+                for name, var in self.metadata.stan_vars.items()
             }
         else:
             return [
                 {
                     name: var.extract_reshape(self._draws[idx])
-                    for name, var in self._metadata.stan_vars.items()
+                    for name, var in self.metadata.stan_vars.items()
                 }
                 for idx in idxs
             ]
@@ -78,15 +113,12 @@ def _assemble_draws(self) -> None:
         if self._draws.shape != (0,):
             return
 
-        csv_file = self.runset.csv_files[0]
         try:
-            *_, draws = stancsv.parse_comments_header_and_draws(
-                self.runset.csv_files[0]
-            )
+            *_, draws = stancsv.parse_comments_header_and_draws(self.csv_file)
             self._draws = stancsv.csv_bytes_list_to_numpy(draws)
         except Exception as exc:
             raise ValueError(
-                f"An error occurred when parsing Stan csv {csv_file}"
+                f"An error occurred when parsing Stan csv {self.csv_file}"
             ) from exc
 
     def stan_variable(self, var: str) -> np.ndarray:
@@ -110,7 +142,7 @@ def stan_variable(self, var: str) -> np.ndarray:
         """
         self._assemble_draws()
         try:
-            out: np.ndarray = self._metadata.stan_vars[var].extract_reshape(
+            out: np.ndarray = self.metadata.stan_vars[var].extract_reshape(
                 self._draws
             )
             return out
@@ -119,7 +151,7 @@ def stan_variable(self, var: str) -> np.ndarray:
             raise ValueError(
                 f'Unknown variable name: {var}\n'
                 'Available variables are '
-                + ", ".join(self._metadata.stan_vars.keys())
+                + ", ".join(self.metadata.stan_vars.keys())
             )
 
     def stan_variables(self) -> dict[str, np.ndarray]:
@@ -140,7 +172,7 @@ def stan_variables(self) -> dict[str, np.ndarray]:
         CmdStanVB.stan_variables
         """
         result = {}
-        for name in self._metadata.stan_vars:
+        for name in self.metadata.stan_vars:
             result[name] = self.stan_variable(name)
         return result
 
@@ -155,7 +187,7 @@ def method_variables(self) -> dict[str, np.ndarray]:
         self._assemble_draws()
         return {
             name: var.extract_reshape(self._draws)
-            for name, var in self._metadata.method_vars.items()
+            for name, var in self.metadata.method_vars.items()
         }
 
     def draws(self) -> np.ndarray:
@@ -181,10 +213,10 @@ def draws_pd(
         cols = []
         if vars is not None:
             for var in dict.fromkeys(vars_list):
-                if var in self._metadata.method_vars:
+                if var in self.metadata.method_vars:
                     cols.append(var)
-                elif var in self._metadata.stan_vars:
-                    info = self._metadata.stan_vars[var]
+                elif var in self.metadata.stan_vars:
+                    info = self.metadata.stan_vars[var]
                     cols.extend(
                         self.column_names[info.start_idx : info.end_idx]
                     )
@@ -216,7 +248,7 @@ def draws_xr(
             )
 
         if vars is None:
-            vars_list = list(self._metadata.stan_vars.keys())
+            vars_list = list(self.metadata.stan_vars.keys())
         elif isinstance(vars, str):
             vars_list = [vars]
         else:
@@ -224,7 +256,7 @@ def draws_xr(
 
         self._assemble_draws()
 
-        meta = self._metadata.cmdstan_config
+        meta = self.metadata.cmdstan_config
         attrs: MutableMapping[Hashable, Any] = {
             "stan_version": f"{meta['stan_version_major']}."
             f"{meta['stan_version_minor']}.{meta['stan_version_patch']}",
@@ -239,7 +271,7 @@ def draws_xr(
         for var in vars_list:
             build_xarray_data(
                 data,
-                self._metadata.stan_vars[var],
+                self.metadata.stan_vars[var],
                 self._draws[:, np.newaxis, :],
             )
         return (
@@ -248,38 +280,20 @@ def draws_xr(
             .squeeze()
         )
 
-    @property
-    def mode(self) -> CmdStanMLE:
-        """
-        Return the maximum a posteriori estimate (mode)
-        as a :class:`CmdStanMLE` object.
-        """
-        return self._mode
-
-    @property
-    def metadata(self) -> InferenceMetadata:
-        """
-        Returns object which contains CmdStan configuration as well as
-        information about the names and structure of the inference method
-        and model output variables.
-        """
-        return self._metadata
-
     def __repr__(self) -> str:
-        mode = '\n'.join(
+        mode_repr = '\n'.join(
             ['\t' + line for line in repr(self.mode).splitlines()]
         )[1:]
-        rep = 'CmdStanLaplace: model={} \nmode=({})\n{}'.format(
-            self.runset.model,
-            mode,
-            self.runset._args.method_args.compose(0, cmd=[]),
-        )
-        rep = '{}\n csv_files:\n\t{}\n output_files:\n\t{}'.format(
-            rep,
-            '\n\t'.join(self.runset.csv_files),
-            '\n\t'.join(self.runset.stdout_files),
-        )
-        return rep
+        lines = [
+            f'CmdStanLaplace: model={self.model_name}',
+            f' mode=({mode_repr})',
+            f' csv_file:\n\t{self.csv_file}',
+        ]
+        if self.config_file is not None:
+            lines.append(f' config_file:\n\t{self.config_file}')
+        if self.stdout_file is not None:
+            lines.append(f' output_file:\n\t{self.stdout_file}')
+        return '\n'.join(lines)
 
     def __getattr__(self, attr: str) -> np.ndarray:
         """Synonymous with ``fit.stan_variable(attr)"""
@@ -307,17 +321,29 @@ def column_names(self) -> tuple[str, ...]:
         and quantities of interest. Corresponds to Stan CSV file header row,
         with names munged to array notation, e.g. `beta[1]` not `beta.1`.
         """
-        return self._metadata.column_names
+        return self.metadata.column_names
 
     def save_csvfiles(self, dir: str | None = None) -> None:
         """
-        Move output CSV files to specified directory.
+        Move output CSV file, and any associated config and stdout files,
+        to the specified directory. Updates the corresponding attributes on
+        this object to point at the new locations.
 
         :param dir: directory path
 
         See Also
         --------
-        stanfit.RunSet.save_csvfiles
         cmdstanpy.from_csv
         """
-        self.runset.save_csvfiles(dir)
+        dest = Path(dir) if dir is not None else Path.cwd()
+        dest.mkdir(parents=True, exist_ok=True)
+
+        for attr in ('csv_file', 'config_file', 'stdout_file'):
+            src = getattr(self, attr)
+            if src is None:
+                continue
+            dst = dest / Path(src).name
+            if dst.exists():
+                raise ValueError(f'File exists, not overwriting: {dst}')
+            shutil.move(src, dst)
+            setattr(self, attr, os.fspath(dst))
diff --git a/test/test_laplace.py b/test/test_laplace.py
index 82ce9364..8fc50b56 100644
--- a/test/test_laplace.py
+++ b/test/test_laplace.py
@@ -33,7 +33,7 @@ def test_laplace_from_csv() -> None:
         data={},
         seed=1234,
     )
-    fit2 = from_csv(fit.runset.csv_files)
+    fit2 = from_csv([fit.csv_file])
     assert isinstance(fit2, cmdstanpy.CmdStanLaplace)
     assert 'x' in fit2.stan_variables()
     assert 'y' in fit2.stan_variables()

From da04db4fdd4d5c09c211af7eea9904ecb4cc6629 Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Wed, 15 Apr 2026 22:30:43 -0400
Subject: [PATCH 14/17] Add default method_type for parse_config

---
 cmdstanpy/stanfit/metadata.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/cmdstanpy/stanfit/metadata.py b/cmdstanpy/stanfit/metadata.py
index 051cd2a1..d8a62707 100644
--- a/cmdstanpy/stanfit/metadata.py
+++ b/cmdstanpy/stanfit/metadata.py
@@ -287,11 +287,16 @@ def flatten_config(data: dict[str, Any]) -> dict[str, Any]:
 
 
 def parse_config(
-    json_data: str | bytes, method_type: type[MethodT]
+    json_data: str | bytes, method_type: type[MethodT] | None = None
 ) -> StanConfig[MethodT]:
-    """Parse a CmdStan config JSON string into a StanConfig with the
-    given method type."""
+    """Parse a CmdStan config JSON string into a StanConfig.
+
+    When ``method_type`` is omitted the method is auto-detected from the
+    JSON content using the discriminated union ``AnyMethodConfig``.
+    """
 
     raw = json.loads(json_data)
     flat = flatten_config(raw)
+    if method_type is None:
+        return StanConfig[AnyMethodConfig].model_validate(flat)  # type: ignore
     return StanConfig[method_type].model_validate(flat)  # type: ignore

From 8b1a39b30030697dedf0bb799b8e0a27eb35c97f Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 18 Apr 2026 13:47:25 -0400
Subject: [PATCH 15/17] Refactor CmdStanMLE

---
 cmdstanpy/model.py            |  28 ++---
 cmdstanpy/stanfit/__init__.py |  65 ++++++-----
 cmdstanpy/stanfit/gq.py       |  10 +-
 cmdstanpy/stanfit/mle.py      | 198 +++++++++++++++++++++-------------
 test/test_optimize.py         |  23 ++--
 5 files changed, 192 insertions(+), 132 deletions(-)

diff --git a/cmdstanpy/model.py b/cmdstanpy/model.py
index ecc277ce..2e23eeb9 100644
--- a/cmdstanpy/model.py
+++ b/cmdstanpy/model.py
@@ -22,7 +22,6 @@
     CmdStanArgs,
     GenerateQuantitiesArgs,
     LaplaceArgs,
-    Method,
     OptimizeArgs,
     PathfinderArgs,
     SamplerArgs,
@@ -434,7 +433,8 @@ def optimize(
             )
         runset.raise_for_timeouts()
 
-        if not runset._check_retcodes():
+        converged = runset._check_retcodes()
+        if not converged:
             msg = "Error during optimization! Command '{}' failed: {}".format(
                 ' '.join(runset.cmd(0)), runset.get_err_msgs()
             )
@@ -442,8 +442,12 @@ def optimize(
                 get_logger().warning(msg)
             else:
                 raise RuntimeError(msg)
-        mle = CmdStanMLE(runset)
-        return mle
+        return CmdStanMLE.from_files(
+            csv_file=runset.csv_files[0],
+            config_file=runset.config_files[0],
+            stdout_file=runset.stdout_files[0],
+            converged=converged,
+        )
 
     # pylint: disable=too-many-arguments
     def sample(
@@ -1040,7 +1044,9 @@ def generate_quantities(
             ),
         ):
             fit_object = previous_fit
-            if isinstance(previous_fit, (CmdStanPathfinder, CmdStanLaplace)):
+            if isinstance(
+                previous_fit, (CmdStanPathfinder, CmdStanLaplace, CmdStanMLE)
+            ):
                 fit_csv_files = [previous_fit.csv_file]
             else:
                 fit_csv_files = previous_fit.runset.csv_files
@@ -1075,7 +1081,7 @@ def generate_quantities(
         elif isinstance(fit_object, CmdStanMLE):
             chains = 1
             chain_ids = [1]
-            if fit_object._save_iterations:
+            if fit_object.config.method_config.save_iterations:
                 get_logger().warning(
                     'MLE contains saved iterations which will be used '
                     'to generate additional quantities of interest.'
@@ -1746,14 +1752,12 @@ def laplace_sample(
         else:
             cmdstan_mode = mode
 
-        if cmdstan_mode.runset.method != Method.OPTIMIZE:
+        if not isinstance(cmdstan_mode, CmdStanMLE):
             raise ValueError(
                 "Mode must be a CmdStanMLE or a path to an optimize CSV"
             )
 
-        mode_jacobian = (
-            cmdstan_mode.runset._args.method_args.jacobian  # type: ignore
-        )
+        mode_jacobian = cmdstan_mode.config.method_config.jacobian
         if mode_jacobian != jacobian:
             raise ValueError(
                 "Jacobian argument to optimize and laplace must match!\n"
@@ -1761,9 +1765,7 @@ def laplace_sample(
                 f"but optimize was run with jacobian={mode_jacobian}"
             )
 
-        laplace_args = LaplaceArgs(
-            cmdstan_mode.runset.csv_files[0], draws, jacobian
-        )
+        laplace_args = LaplaceArgs(cmdstan_mode.csv_file, draws, jacobian)
 
         with temp_single_json(data) as _data:
             args = CmdStanArgs(
diff --git a/cmdstanpy/stanfit/__init__.py b/cmdstanpy/stanfit/__init__.py
index 1729aec7..d156114d 100644
--- a/cmdstanpy/stanfit/__init__.py
+++ b/cmdstanpy/stanfit/__init__.py
@@ -3,12 +3,7 @@
 import glob
 import os
 
-from cmdstanpy.cmdstan_args import (
-    CmdStanArgs,
-    OptimizeArgs,
-    SamplerArgs,
-    VariationalArgs,
-)
+from cmdstanpy.cmdstan_args import CmdStanArgs, SamplerArgs, VariationalArgs
 from cmdstanpy.utils import check_sampler_csv, get_logger, stancsv
 
 from .gq import CmdStanGQ, PrevFit
@@ -33,7 +28,7 @@
 ]
 
 
-def from_csv(
+def from_csv(  # pylint: disable=too-many-return-statements
     path: str | list[str] | os.PathLike | None = None,
     method: str | None = None,
 ) -> (
@@ -177,32 +172,52 @@ def from_csv(
             fit.draws()
             return fit
         elif config_dict['method'] == 'optimize':
+            if len(csvfiles) != 1:
+                raise ValueError(
+                    'Expecting a single optimize Stan CSV file, '
+                    f'found {len(csvfiles)}'
+                )
+            csv_file = csvfiles[0]
+            config_file = os.path.splitext(csv_file)[0] + '_config.json'
+            if os.path.exists(config_file):
+                return CmdStanMLE.from_files(
+                    csv_file=csv_file, config_file=config_file
+                )
+            # Legacy path: no config file, build config from CSV metadata
             if 'algorithm' not in config_dict:
                 raise ValueError(
                     "Cannot find optimization algorithm in file {}.".format(
-                        csvfiles[0]
+                        csv_file
                     )
                 )
-            algorithm: str = config_dict['algorithm']  # type: ignore
-            save_iterations = config_dict['save_iterations'] == 1
-            jacobian = config_dict.get('jacobian', 0) == 1
+            from .metadata import OptimizeConfig, StanConfig
 
-            optimize_args = OptimizeArgs(
-                algorithm=algorithm,
-                save_iterations=save_iterations,
-                jacobian=jacobian,
+            opt_config = OptimizeConfig(
+                algorithm=config_dict['algorithm'],  # type: ignore
+                save_iterations=config_dict.get('save_iterations', 0) == 1,
+                jacobian=config_dict.get('jacobian', 0) == 1,
             )
-            cmdstan_args = CmdStanArgs(
-                model_name=model,
-                model_exe=model,
-                chain_ids=None,
-                method_args=optimize_args,
+            stan_config = StanConfig[OptimizeConfig].model_validate(
+                {
+                    'model_name': config_dict['model'],
+                    'stan_major_version': str(
+                        config_dict.get('stan_version_major', '')
+                    ),
+                    'stan_minor_version': str(
+                        config_dict.get('stan_version_minor', '')
+                    ),
+                    'stan_patch_version': str(
+                        config_dict.get('stan_version_patch', '')
+                    ),
+                    'method_config': opt_config.model_dump(),
+                }
+            )
+            return CmdStanMLE(
+                metadata=InferenceMetadata.from_csv(csv_file),
+                model_name=stan_config.model_name,
+                csv_file=csv_file,
+                config=stan_config,
             )
-            runset = RunSet(args=cmdstan_args)
-            runset._csv_files = csvfiles
-            for i in range(len(runset._retcodes)):
-                runset._set_retcode(i, 0)
-            return CmdStanMLE(runset)
         elif config_dict['method'] == 'variational':
             if 'algorithm' not in config_dict:
                 raise ValueError(
diff --git a/cmdstanpy/stanfit/gq.py b/cmdstanpy/stanfit/gq.py
index 1937cc32..7cbf09d6 100644
--- a/cmdstanpy/stanfit/gq.py
+++ b/cmdstanpy/stanfit/gq.py
@@ -209,7 +209,7 @@ def draws(
                 )
             elif (
                 isinstance(self.previous_fit, CmdStanMLE)
-                and not self.previous_fit._save_iterations
+                and not self.previous_fit.config.method_config.save_iterations
             ):
                 get_logger().warning(
                     "MLE doesn't contain draws from pre-convergence iterations,"
@@ -301,7 +301,7 @@ def draws_pd(
                 )
             elif (
                 isinstance(self.previous_fit, CmdStanMLE)
-                and not self.previous_fit._save_iterations
+                and not self.previous_fit.config.method_config.save_iterations
             ):
                 get_logger().warning(
                     "MLE doesn't contain draws from pre-convergence iterations,"
@@ -649,7 +649,7 @@ def _draws_start(self, inc_warmup: bool) -> tuple[int, int]:
 
         elif isinstance(p_fit, CmdStanMLE):
             num_draws = 1
-            if p_fit._save_iterations:
+            if p_fit.config.method_config.save_iterations:
                 opt_iters = len(p_fit.optimized_iterations_np)  # type: ignore
                 if inc_warmup:
                     num_draws = opt_iters
@@ -675,7 +675,7 @@ def _previous_draws(self, inc_warmup: bool) -> np.ndarray:
         if isinstance(p_fit, CmdStanMCMC):
             return p_fit.draws(inc_warmup=inc_warmup)
         elif isinstance(p_fit, CmdStanMLE):
-            if inc_warmup and p_fit._save_iterations:
+            if inc_warmup and p_fit.config.method_config.save_iterations:
                 return p_fit.optimized_iterations_np[:, None]  # type: ignore
 
             return np.atleast_2d(  # type: ignore
@@ -703,7 +703,7 @@ def _previous_draws_pd(
             return p_fit.draws_pd(vars or None, inc_warmup=inc_warmup)
 
         elif isinstance(p_fit, CmdStanMLE):
-            if inc_warmup and p_fit._save_iterations:
+            if inc_warmup and p_fit.config.method_config.save_iterations:
                 return p_fit.optimized_iterations_pd[sel]  # type: ignore
             else:
                 return p_fit.optimized_params_pd[sel]
diff --git a/cmdstanpy/stanfit/mle.py b/cmdstanpy/stanfit/mle.py
index 03b44799..fb646fd3 100644
--- a/cmdstanpy/stanfit/mle.py
+++ b/cmdstanpy/stanfit/mle.py
@@ -1,60 +1,86 @@
 """Container for the result of running optimization"""
 
+from __future__ import annotations
+
+import os
+import shutil
 from collections import OrderedDict
+from dataclasses import dataclass, field
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
 
-from cmdstanpy.cmdstan_args import Method, OptimizeArgs
 from cmdstanpy.utils import get_logger, stancsv
 
-from .metadata import InferenceMetadata
-from .runset import RunSet
+from .metadata import (
+    InferenceMetadata,
+    OptimizeConfig,
+    StanConfig,
+    parse_config,
+)
 
 
+@dataclass
 class CmdStanMLE:
     """
     Container for outputs from CmdStan optimization.
     Created by :meth:`CmdStanModel.optimize`.
     """
 
-    def __init__(self, runset: RunSet) -> None:
-        """Initialize object."""
-        if not runset.method == Method.OPTIMIZE:
-            raise ValueError(
-                'Wrong runset method, expecting optimize runset, '
-                'found method {}'.format(runset.method)
-            )
-        self.runset = runset
-        # info from runset to be exposed
-        self.converged = runset._check_retcodes()
-        optimize_args = self.runset._args.method_args
-        assert isinstance(
-            optimize_args, OptimizeArgs
-        )  # make the typechecker happy
-        self._save_iterations: bool = optimize_args.save_iterations
-
-        csv_file = self.runset.csv_files[0]
+    metadata: InferenceMetadata
+    model_name: str
+    csv_file: str
+    config: StanConfig[OptimizeConfig]
+    converged: bool = True
+    config_file: str | None = None  # None if config object passed directly
+    stdout_file: str | None = None
+    _mle: np.ndarray = field(default_factory=lambda: np.array(()), init=False)
+    _all_iters: np.ndarray = field(
+        default_factory=lambda: np.array(()), init=False
+    )
+
+    @classmethod
+    def from_files(
+        cls,
+        csv_file: str | os.PathLike,
+        config_file: str | os.PathLike,
+        stdout_file: str | os.PathLike | None = None,
+        converged: bool = True,
+    ) -> CmdStanMLE:
+        with open(config_file) as f:
+            stan_config = parse_config(f.read(), OptimizeConfig)
+
+        metadata = InferenceMetadata.from_csv(csv_file)
+        return cls(
+            metadata=metadata,
+            model_name=stan_config.model_name,
+            csv_file=os.fspath(csv_file),
+            config=stan_config,
+            converged=converged,
+            config_file=os.fspath(config_file),
+            stdout_file=(
+                os.fspath(stdout_file) if stdout_file is not None else None
+            ),
+        )
+
+    def _assemble_draws(self) -> None:
+        if self._mle.shape != (0,):
+            return
+
         try:
-            (
-                comment_lines,
-                header,
-                draws_lines,
-            ) = stancsv.parse_comments_header_and_draws(
-                self.runset.csv_files[0]
-            )
-            self._metadata = InferenceMetadata(
-                stancsv.construct_config_header_dict(comment_lines, header)
+            *_, draws_lines = stancsv.parse_comments_header_and_draws(
+                self.csv_file
             )
             all_draws = stancsv.csv_bytes_list_to_numpy(draws_lines)
-
         except Exception as exc:
             raise ValueError(
-                f"An error occurred when parsing Stan csv {csv_file}"
+                f"An error occurred when parsing Stan csv {self.csv_file}"
             ) from exc
-        self._mle: np.ndarray = all_draws[-1]
-        if self._save_iterations:
-            self._all_iters: np.ndarray = all_draws
+
+        self._mle = all_draws[-1]
+        if self.config.method_config.save_iterations:
+            self._all_iters = all_draws
 
     def create_inits(
         self, seed: int | None = None, chains: int = 4
@@ -79,18 +105,21 @@ def create_inits(
         return self.stan_variables()
 
     def __repr__(self) -> str:
-        repr = 'CmdStanMLE: model={}{}'.format(
-            self.runset.model, self.runset._args.method_args.compose(0, cmd=[])
-        )
-        repr = '{}\n csv_file:\n\t{}\n output_file:\n\t{}'.format(
-            repr,
-            '\n\t'.join(self.runset.csv_files),
-            '\n\t'.join(self.runset.stdout_files),
-        )
+        mc = self.config.method_config
+        lines = [
+            f'CmdStanMLE: model={self.model_name}'
+            f' method={mc.method} algorithm={mc.algorithm}',
+            f' csv_file:\n\t{self.csv_file}',
+        ]
+        if self.config_file is not None:
+            lines.append(f' config_file:\n\t{self.config_file}')
+        if self.stdout_file is not None:
+            lines.append(f' output_file:\n\t{self.stdout_file}')
         if not self.converged:
-            repr = '{}\n Warning: invalid estimate, '.format(repr)
-            repr = '{} optimization failed to converge.'.format(repr)
-        return repr
+            lines.append(
+                ' Warning: invalid estimate, optimization failed to converge.'
+            )
+        return '\n'.join(lines)
 
     def __getattr__(self, attr: str) -> np.ndarray:
         """Synonymous with ``fit.stan_variable(attr)"""
@@ -102,6 +131,23 @@ def __getattr__(self, attr: str) -> np.ndarray:
             # pylint: disable=raise-missing-from
             raise AttributeError(*e.args)
 
+    def __getstate__(self) -> dict:
+        # This function returns the mapping of objects to serialize with pickle.
+        # See https://docs.python.org/3/library/pickle.html#object.__getstate__
+        # for details. We call _assemble_draws to ensure estimates have been
+        # loaded prior to serialization.
+        self._assemble_draws()
+        state = self.__dict__.copy()
+        # StanConfig[OptimizeConfig] is a generic alias with no module-level
+        # name, so we serialize it as a plain dict for pickle compatibility.
+        state['config'] = self.config.model_dump()
+        return state
+
+    def __setstate__(self, state: dict) -> None:
+        config_dict = state.pop('config')
+        self.__dict__.update(state)
+        self.config = StanConfig[OptimizeConfig].model_validate(config_dict)
+
     @property
     def column_names(self) -> tuple[str, ...]:
         """
@@ -110,15 +156,6 @@ def column_names(self) -> tuple[str, ...]:
         """
         return self.metadata.column_names
 
-    @property
-    def metadata(self) -> InferenceMetadata:
-        """
-        Returns object which contains CmdStan configuration as well as
-        information about the names and structure of the inference method
-        and model output variables.
-        """
-        return self._metadata
-
     @property
     def optimized_params_np(self) -> np.ndarray:
         """
@@ -130,6 +167,7 @@ def optimized_params_np(self) -> np.ndarray:
             get_logger().warning(
                 'Invalid estimate, optimization failed to converge.'
             )
+        self._assemble_draws()
         return self._mle
 
     @property
@@ -140,7 +178,7 @@ def optimized_iterations_np(self) -> np.ndarray | None:
         the value for `lp__` as well as all Stan program variables.
 
         """
-        if not self._save_iterations:
+        if not self.config.method_config.save_iterations:
             get_logger().warning(
                 'Intermediate iterations not saved to CSV output file. '
                 'Rerun the optimize method with "save_iterations=True".'
@@ -150,6 +188,7 @@ def optimized_iterations_np(self) -> np.ndarray | None:
             get_logger().warning(
                 'Invalid estimate, optimization failed to converge.'
             )
+        self._assemble_draws()
         return self._all_iters
 
     @property
@@ -159,10 +198,11 @@ def optimized_params_pd(self) -> pd.DataFrame:
         which contains all optimizer outputs, i.e., the value for `lp__`
         as well as all Stan program variables.
         """
-        if not self.runset._check_retcodes():
+        if not self.converged:
             get_logger().warning(
                 'Invalid estimate, optimization failed to converge.'
             )
+        self._assemble_draws()
         return pd.DataFrame([self._mle], columns=self.column_names)
 
     @property
@@ -173,7 +213,7 @@ def optimized_iterations_pd(self) -> pd.DataFrame | None:
         the value for `lp__` as well as all Stan program variables.
 
         """
-        if not self._save_iterations:
+        if not self.config.method_config.save_iterations:
             get_logger().warning(
                 'Intermediate iterations not saved to CSV output file. '
                 'Rerun the optimize method with "save_iterations=True".'
@@ -183,6 +223,7 @@ def optimized_iterations_pd(self) -> pd.DataFrame | None:
             get_logger().warning(
                 'Invalid estimate, optimization failed to converge.'
             )
+        self._assemble_draws()
         return pd.DataFrame(self._all_iters, columns=self.column_names)
 
     @property
@@ -191,10 +232,11 @@ def optimized_params_dict(self) -> dict[str, np.float64]:
         Returns all estimates from the optimizer, including `lp__` as a
         Python Dict.  Only returns estimate from final iteration.
         """
-        if not self.runset._check_retcodes():
+        if not self.converged:
             get_logger().warning(
                 'Invalid estimate, optimization failed to converge.'
             )
+        self._assemble_draws()
         return OrderedDict(zip(self.column_names, self._mle))
 
     def stan_variable(
@@ -228,36 +270,36 @@ def stan_variable(
         CmdStanGQ.stan_variable
         CmdStanLaplace.stan_variable
         """
-        if var not in self._metadata.stan_vars:
+        if var not in self.metadata.stan_vars:
             raise ValueError(
                 f'Unknown variable name: {var}\n'
-                'Available variables are ' + ", ".join(self._metadata.stan_vars)
+                'Available variables are ' + ", ".join(self.metadata.stan_vars)
             )
-        if warn and inc_iterations and not self._save_iterations:
+        save_iterations = self.config.method_config.save_iterations
+        if warn and inc_iterations and not save_iterations:
             get_logger().warning(
                 'Intermediate iterations not saved to CSV output file. '
                 'Rerun the optimize method with "save_iterations=True".'
             )
-        if warn and not self.runset._check_retcodes():
+        if warn and not self.converged:
             get_logger().warning(
                 'Invalid estimate, optimization failed to converge.'
             )
-        if inc_iterations and self._save_iterations:
+        self._assemble_draws()
+        if inc_iterations and save_iterations:
             data = self._all_iters
         else:
             data = self._mle
 
         try:
-            out: np.ndarray = self._metadata.stan_vars[var].extract_reshape(
-                data
-            )
+            out: np.ndarray = self.metadata.stan_vars[var].extract_reshape(data)
             return out
         except KeyError:
             # pylint: disable=raise-missing-from
             raise ValueError(
                 f'Unknown variable name: {var}\n'
                 'Available variables are '
-                + ", ".join(self._metadata.stan_vars.keys())
+                + ", ".join(self.metadata.stan_vars.keys())
             )
 
     def stan_variables(
@@ -282,12 +324,12 @@ def stan_variables(
         CmdStanGQ.stan_variables
         CmdStanLaplace.stan_variables
         """
-        if not self.runset._check_retcodes():
+        if not self.converged:
             get_logger().warning(
                 'Invalid estimate, optimization failed to converge.'
             )
         result = {}
-        for name in self._metadata.stan_vars:
+        for name in self.metadata.stan_vars:
             result[name] = self.stan_variable(
                 name, inc_iterations=inc_iterations, warn=False
             )
@@ -295,13 +337,25 @@ def stan_variables(
 
     def save_csvfiles(self, dir: str | None = None) -> None:
         """
-        Move output CSV files to specified directory.
+        Move output CSV file, and any associated config and stdout files,
+        to the specified directory. Updates the corresponding attributes on
+        this object to point at the new locations.
 
         :param dir: directory path
 
         See Also
         --------
-        stanfit.RunSet.save_csvfiles
         cmdstanpy.from_csv
         """
-        self.runset.save_csvfiles(dir)
+        dest = Path(dir) if dir is not None else Path.cwd()
+        dest.mkdir(parents=True, exist_ok=True)
+
+        for attr in ('csv_file', 'config_file', 'stdout_file'):
+            src = getattr(self, attr)
+            if src is None:
+                continue
+            dst = dest / Path(src).name
+            if dst.exists():
+                raise ValueError(f'File exists, not overwriting: {dst}')
+            shutil.move(src, dst)
+            setattr(self, attr, os.fspath(dst))
diff --git a/test/test_optimize.py b/test/test_optimize.py
index 2f255468..4daf56fd 100644
--- a/test/test_optimize.py
+++ b/test/test_optimize.py
@@ -13,30 +13,19 @@
 import numpy as np
 import pytest
 
-from cmdstanpy.cmdstan_args import CmdStanArgs, OptimizeArgs
 from cmdstanpy.model import CmdStanModel
-from cmdstanpy.stanfit import CmdStanMLE, RunSet, from_csv
+from cmdstanpy.stanfit import CmdStanMLE, from_csv
 
 HERE = os.path.dirname(os.path.abspath(__file__))
 DATAFILES_PATH = os.path.join(HERE, 'data')
 
 
 def test_instantiate() -> None:
-    stan = os.path.join(DATAFILES_PATH, 'optimize', 'rosenbrock.stan')
-    model = CmdStanModel(stan_file=stan)
-    args = OptimizeArgs(algorithm='Newton')
-    cmdstan_args = CmdStanArgs(
-        model_name=model.name,
-        model_exe=model.exe_file,
-        chain_ids=None,
-        data={},
-        method_args=args,
+    csvfiles_path = os.path.join(
+        DATAFILES_PATH, 'optimize', 'rosenbrock_mle.csv'
     )
-    runset = RunSet(args=cmdstan_args, chains=1)
-    runset._csv_files = [
-        os.path.join(DATAFILES_PATH, 'optimize', 'rosenbrock_mle.csv')
-    ]
-    mle = CmdStanMLE(runset)
+    mle = from_csv(path=csvfiles_path)
+    assert isinstance(mle, CmdStanMLE)
     assert 'CmdStanMLE: model=rosenbrock' in repr(mle)
     assert 'method=optimize' in repr(mle)
     assert mle.column_names == ('lp__', 'x', 'y')
@@ -664,7 +653,7 @@ def test_serialization() -> None:
         history_size=5,
     )
     dumped = pickle.dumps(mle1)
-    shutil.rmtree(mle1.runset._outdir)
+    shutil.rmtree(os.path.dirname(mle1.csv_file))
     mle2: CmdStanMLE = pickle.loads(dumped)
     np.testing.assert_array_equal(
         mle1.optimized_params_np, mle2.optimized_params_np

From 34660248f07c7dd9b8c8c44776b8371c9d45a10a Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sat, 18 Apr 2026 14:06:29 -0400
Subject: [PATCH 16/17] Allow passing 'mode' directly to
 CmdStanLaplace.from_files

---
 cmdstanpy/model.py           |  1 +
 cmdstanpy/stanfit/laplace.py | 13 ++++++++++---
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/cmdstanpy/model.py b/cmdstanpy/model.py
index 2e23eeb9..f3fee26c 100644
--- a/cmdstanpy/model.py
+++ b/cmdstanpy/model.py
@@ -1793,6 +1793,7 @@ def laplace_sample(
             csv_file=runset.csv_files[0],
             config_file=runset.config_files[0],
             stdout_file=runset.stdout_files[0],
+            mode=cmdstan_mode,
         )
 
     def _run_cmdstan(
diff --git a/cmdstanpy/stanfit/laplace.py b/cmdstanpy/stanfit/laplace.py
index c1277133..7913fc21 100644
--- a/cmdstanpy/stanfit/laplace.py
+++ b/cmdstanpy/stanfit/laplace.py
@@ -53,6 +53,7 @@ def from_files(
         csv_file: str | os.PathLike,
         config_file: str | os.PathLike,
         stdout_file: str | os.PathLike | None = None,
+        mode: CmdStanMLE | None = None,
     ) -> CmdStanLaplace:
         # Local import to avoid circular dependency with stanfit.__init__
         from cmdstanpy.stanfit import from_csv
@@ -61,9 +62,15 @@ def from_files(
             stan_config = parse_config(f.read(), LaplaceConfig)
 
         metadata = InferenceMetadata.from_csv(csv_file)
-        # temporary - should have done CmdStanMLE first
-        mode = from_csv(stan_config.method_config.mode, method='optimize')
-        assert isinstance(mode, CmdStanMLE)
+        if mode is None:
+            mle = from_csv(stan_config.method_config.mode, method='optimize')
+            if not isinstance(mle, CmdStanMLE):
+                raise TypeError(
+                    f'Expected CmdStanMLE from mode file '
+                    f'{stan_config.method_config.mode!r}, got '
+                    f'{type(mle).__name__}'
+                )
+            mode = mle
         return cls(
             metadata=metadata,
             csv_file=os.fspath(csv_file),

From 16eff3dc40b710c3e1a3011c6966a7c2534b226f Mon Sep 17 00:00:00 2001
From: amas <andrew.mascioli1@gmail.com>
Date: Sun, 3 May 2026 12:49:37 -0400
Subject: [PATCH 17/17] Refactor CmdStanVB to build from files

---
 cmdstanpy/model.py                            |   11 +-
 cmdstanpy/stanfit/__init__.py                 |   38 +-
 cmdstanpy/stanfit/vb.py                       |  175 ++-
 .../data/variational/eta_should_be_big_vb.csv | 1039 +++++++++++++++++
 .../eta_should_be_big_vb_config.json          |   49 +
 test/test_compliance.py                       |    8 +-
 test/test_variational.py                      |   57 +-
 7 files changed, 1236 insertions(+), 141 deletions(-)
 create mode 100644 test/data/variational/eta_should_be_big_vb.csv
 create mode 100644 test/data/variational/eta_should_be_big_vb_config.json

diff --git a/cmdstanpy/model.py b/cmdstanpy/model.py
index f3fee26c..74111eb1 100644
--- a/cmdstanpy/model.py
+++ b/cmdstanpy/model.py
@@ -1045,7 +1045,8 @@ def generate_quantities(
         ):
             fit_object = previous_fit
             if isinstance(
-                previous_fit, (CmdStanPathfinder, CmdStanLaplace, CmdStanMLE)
+                previous_fit,
+                (CmdStanPathfinder, CmdStanLaplace, CmdStanMLE, CmdStanVB),
             ):
                 fit_csv_files = [previous_fit.csv_file]
             else:
@@ -1338,9 +1339,11 @@ def variational(
                     runset.get_err_msgs()
                 )
             raise RuntimeError(msg)
-        # pylint: disable=invalid-name
-        vb = CmdStanVB(runset)
-        return vb
+        return CmdStanVB.from_files(
+            csv_file=runset.csv_files[0],
+            config_file=runset.config_files[0],
+            stdout_file=runset.stdout_files[0],
+        )
 
     def pathfinder(
         self,
diff --git a/cmdstanpy/stanfit/__init__.py b/cmdstanpy/stanfit/__init__.py
index d156114d..5328f3dd 100644
--- a/cmdstanpy/stanfit/__init__.py
+++ b/cmdstanpy/stanfit/__init__.py
@@ -3,7 +3,7 @@
 import glob
 import os
 
-from cmdstanpy.cmdstan_args import CmdStanArgs, SamplerArgs, VariationalArgs
+from cmdstanpy.cmdstan_args import CmdStanArgs, SamplerArgs
 from cmdstanpy.utils import check_sampler_csv, get_logger, stancsv
 
 from .gq import CmdStanGQ, PrevFit
@@ -219,33 +219,21 @@ def from_csv(  # pylint: disable=too-many-return-statements
                 config=stan_config,
             )
         elif config_dict['method'] == 'variational':
-            if 'algorithm' not in config_dict:
+            if len(csvfiles) != 1:
                 raise ValueError(
-                    "Cannot find variational algorithm in file {}.".format(
-                        csvfiles[0]
-                    )
+                    'Expecting a single variational Stan CSV file, '
+                    f'found {len(csvfiles)}'
                 )
-            variational_args = VariationalArgs(
-                algorithm=config_dict['algorithm'],  # type: ignore
-                iter=config_dict['iter'],  # type: ignore
-                grad_samples=config_dict['grad_samples'],  # type: ignore
-                elbo_samples=config_dict['elbo_samples'],  # type: ignore
-                eta=config_dict['eta'],  # type: ignore
-                tol_rel_obj=config_dict['tol_rel_obj'],  # type: ignore
-                eval_elbo=config_dict['eval_elbo'],  # type: ignore
-                output_samples=config_dict['output_samples'],  # type: ignore
-            )
-            cmdstan_args = CmdStanArgs(
-                model_name=model,
-                model_exe=model,
-                chain_ids=None,
-                method_args=variational_args,
+            csv_file = csvfiles[0]
+            config_file = os.path.splitext(csv_file)[0] + '_config.json'
+            if not os.path.exists(config_file):
+                raise ValueError(
+                    'Variational config file not found at expected path: '
+                    f'{config_file}'
+                )
+            return CmdStanVB.from_files(
+                csv_file=csv_file, config_file=config_file
             )
-            runset = RunSet(args=cmdstan_args)
-            runset._csv_files = csvfiles
-            for i in range(len(runset._retcodes)):
-                runset._set_retcode(i, 0)
-            return CmdStanVB(runset)
         elif config_dict['method'] == 'laplace':
             if len(csvfiles) != 1:
                 raise ValueError(
diff --git a/cmdstanpy/stanfit/vb.py b/cmdstanpy/stanfit/vb.py
index 7a1f59b0..09464262 100644
--- a/cmdstanpy/stanfit/vb.py
+++ b/cmdstanpy/stanfit/vb.py
@@ -1,55 +1,88 @@
 """Container for the results of running autodiff variational inference"""
 
+from __future__ import annotations
+
+import os
+import shutil
 from collections import OrderedDict
+from dataclasses import dataclass, field
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
 
-from cmdstanpy.cmdstan_args import Method
 from cmdstanpy.utils import stancsv
 
-from .metadata import InferenceMetadata
-from .runset import RunSet
+from .metadata import (
+    InferenceMetadata,
+    StanConfig,
+    VariationalConfig,
+    parse_config,
+)
 
 
+@dataclass
 class CmdStanVB:
     """
     Container for outputs from CmdStan variational run.
     Created by :meth:`CmdStanModel.variational`.
     """
 
-    def __init__(self, runset: RunSet) -> None:
-        """Initialize object."""
-        if not runset.method == Method.VARIATIONAL:
-            raise ValueError(
-                'Wrong runset method, expecting variational inference, '
-                'found method {}'.format(runset.method)
-            )
-        self.runset = runset
+    metadata: InferenceMetadata
+    model_name: str
+    csv_file: str
+    config: StanConfig[VariationalConfig]
+    config_file: str | None = None  # None if config object passed directly
+    stdout_file: str | None = None
+    _variational_mean: np.ndarray = field(
+        default_factory=lambda: np.array(()), init=False
+    )
+    _variational_sample: np.ndarray = field(
+        default_factory=lambda: np.array(()), init=False
+    )
+    _eta: float | None = field(default=None, init=False)
+
+    @classmethod
+    def from_files(
+        cls,
+        csv_file: str | os.PathLike,
+        config_file: str | os.PathLike,
+        stdout_file: str | os.PathLike | None = None,
+    ) -> CmdStanVB:
+        with open(config_file) as f:
+            stan_config = parse_config(f.read(), VariationalConfig)
+
+        metadata = InferenceMetadata.from_csv(csv_file)
+        return cls(
+            metadata=metadata,
+            model_name=stan_config.model_name,
+            csv_file=os.fspath(csv_file),
+            config=stan_config,
+            config_file=os.fspath(config_file),
+            stdout_file=(
+                os.fspath(stdout_file) if stdout_file is not None else None
+            ),
+        )
+
+    def _assemble_draws(self) -> None:
+        if self._variational_mean.shape != (0,):
+            return
 
-        csv_file = self.runset.csv_files[0]
         try:
             (
                 comment_lines,
-                header,
+                _,
                 draw_lines,
-            ) = stancsv.parse_comments_header_and_draws(
-                self.runset.csv_files[0]
-            )
-
-            self._metadata = InferenceMetadata(
-                stancsv.construct_config_header_dict(comment_lines, header)
-            )
+            ) = stancsv.parse_comments_header_and_draws(self.csv_file)
             self._eta = stancsv.parse_variational_eta(comment_lines)
-
             draws_np = stancsv.csv_bytes_list_to_numpy(draw_lines)
-
         except Exception as exc:
             raise ValueError(
-                f"An error occurred when parsing Stan csv {csv_file}"
+                f"An error occurred when parsing Stan csv {self.csv_file}"
             ) from exc
-        self._variational_mean: np.ndarray = draws_np[0]
-        self._variational_sample: np.ndarray = draws_np[1:]
+
+        self._variational_mean = draws_np[0]
+        self._variational_sample = draws_np[1:]
 
     def create_inits(
         self, seed: int | None = None, chains: int = 4
@@ -67,36 +100,38 @@ def create_inits(
         of dictionaries is returned, in the format expected for the
         ``inits`` argument of :meth:`CmdStanModel.sample`.
         """
+        self._assemble_draws()
         rng = np.random.default_rng(seed)
         idxs = rng.choice(
-            self.variational_sample.shape[0], size=chains, replace=False
+            self._variational_sample.shape[0], size=chains, replace=False
         )
         if chains == 1:
-            draw = self.variational_sample[idxs[0]]
+            draw = self._variational_sample[idxs[0]]
             return {
                 name: var.extract_reshape(draw)
-                for name, var in self._metadata.stan_vars.items()
+                for name, var in self.metadata.stan_vars.items()
             }
         else:
             return [
                 {
-                    name: var.extract_reshape(self.variational_sample[idx])
-                    for name, var in self._metadata.stan_vars.items()
+                    name: var.extract_reshape(self._variational_sample[idx])
+                    for name, var in self.metadata.stan_vars.items()
                 }
                 for idx in idxs
             ]
 
     def __repr__(self) -> str:
-        repr = 'CmdStanVB: model={}{}'.format(
-            self.runset.model, self.runset._args.method_args.compose(0, cmd=[])
-        )
-        repr = '{}\n csv_file:\n\t{}\n output_file:\n\t{}'.format(
-            repr,
-            '\n\t'.join(self.runset.csv_files),
-            '\n\t'.join(self.runset.stdout_files),
-        )
-        # TODO - diagnostic, profiling files
-        return repr
+        mc = self.config.method_config
+        lines = [
+            f'CmdStanVB: model={self.model_name}'
+            f' method={mc.method} algorithm={mc.algorithm}',
+            f' csv_file:\n\t{self.csv_file}',
+        ]
+        if self.config_file is not None:
+            lines.append(f' config_file:\n\t{self.config_file}')
+        if self.stdout_file is not None:
+            lines.append(f' output_file:\n\t{self.stdout_file}')
+        return '\n'.join(lines)
 
     def __getattr__(self, attr: str) -> np.ndarray:
         """Synonymous with ``fit.stan_variable(attr)"""
@@ -108,6 +143,23 @@ def __getattr__(self, attr: str) -> np.ndarray:
             # pylint: disable=raise-missing-from
             raise AttributeError(*e.args)
 
+    def __getstate__(self) -> dict:
+        # This function returns the mapping of objects to serialize with pickle.
+        # See https://docs.python.org/3/library/pickle.html#object.__getstate__
+        # for details. We call _assemble_draws to ensure draws have been
+        # loaded prior to serialization.
+        self._assemble_draws()
+        state = self.__dict__.copy()
+        # StanConfig[VariationalConfig] is a generic alias with no module-level
+        # name, so we serialize it as a plain dict for pickle compatibility.
+        state['config'] = self.config.model_dump()
+        return state
+
+    def __setstate__(self, state: dict) -> None:
+        config_dict = state.pop('config')
+        self.__dict__.update(state)
+        self.config = StanConfig[VariationalConfig].model_validate(config_dict)
+
     @property
     def columns(self) -> int:
         """
@@ -131,13 +183,15 @@ def eta(self) -> float:
         """
         Step size scaling parameter 'eta'
         """
-        return self._eta
+        self._assemble_draws()
+        return self._eta  # type: ignore[return-value]
 
     @property
     def variational_params_np(self) -> np.ndarray:
         """
         Returns inferred parameter means as numpy array.
         """
+        self._assemble_draws()
         return self._variational_mean
 
     @property
@@ -145,22 +199,15 @@ def variational_params_pd(self) -> pd.DataFrame:
         """
         Returns inferred parameter means as pandas DataFrame.
         """
+        self._assemble_draws()
         return pd.DataFrame([self._variational_mean], columns=self.column_names)
 
     @property
     def variational_params_dict(self) -> dict[str, np.ndarray]:
         """Returns inferred parameter means as Dict."""
+        self._assemble_draws()
         return OrderedDict(zip(self.column_names, self._variational_mean))
 
-    @property
-    def metadata(self) -> InferenceMetadata:
-        """
-        Returns object which contains CmdStan configuration as well as
-        information about the names and structure of the inference method
-        and model output variables.
-        """
-        return self._metadata
-
     def stan_variable(self, var: str, *, mean: bool = False) -> np.ndarray:
         """
         Return a numpy.ndarray which contains the estimates for the
@@ -195,6 +242,7 @@ def stan_variable(self, var: str, *, mean: bool = False) -> np.ndarray:
         CmdStanGQ.stan_variable
         CmdStanLaplace.stan_variable
         """
+        self._assemble_draws()
 
         if mean:
             draws = self._variational_mean
@@ -202,17 +250,16 @@ def stan_variable(self, var: str, *, mean: bool = False) -> np.ndarray:
             draws = self._variational_sample
 
         try:
-            out: np.ndarray = self._metadata.stan_vars[var].extract_reshape(
+            out: np.ndarray = self.metadata.stan_vars[var].extract_reshape(
                 draws
             )
-
             return out
         except KeyError:
             # pylint: disable=raise-missing-from
             raise ValueError(
                 f'Unknown variable name: {var}\n'
                 'Available variables are '
-                + ", ".join(self._metadata.stan_vars.keys())
+                + ", ".join(self.metadata.stan_vars.keys())
             )
 
     def stan_variables(self, *, mean: bool = False) -> dict[str, np.ndarray]:
@@ -230,13 +277,14 @@ def stan_variables(self, *, mean: bool = False) -> dict[str, np.ndarray]:
         CmdStanLaplace.stan_variables
         """
         result = {}
-        for name in self._metadata.stan_vars:
+        for name in self.metadata.stan_vars:
             result[name] = self.stan_variable(name, mean=mean)
         return result
 
     @property
     def variational_sample(self) -> np.ndarray:
         """Returns the set of approximate posterior output draws."""
+        self._assemble_draws()
         return self._variational_sample
 
     @property
@@ -245,17 +293,30 @@ def variational_sample_pd(self) -> pd.DataFrame:
         Returns the set of approximate posterior output draws as
         a pandas DataFrame.
         """
+        self._assemble_draws()
         return pd.DataFrame(self._variational_sample, columns=self.column_names)
 
     def save_csvfiles(self, dir: str | None = None) -> None:
         """
-        Move output CSV files to specified directory.
+        Move output CSV file, and any associated config and stdout files,
+        to the specified directory. Updates the corresponding attributes on
+        this object to point at the new locations.
 
         :param dir: directory path
 
         See Also
         --------
-        stanfit.RunSet.save_csvfiles
         cmdstanpy.from_csv
         """
-        self.runset.save_csvfiles(dir)
+        dest = Path(dir) if dir is not None else Path.cwd()
+        dest.mkdir(parents=True, exist_ok=True)
+
+        for attr in ('csv_file', 'config_file', 'stdout_file'):
+            src = getattr(self, attr)
+            if src is None:
+                continue
+            dst = dest / Path(src).name
+            if dst.exists():
+                raise ValueError(f'File exists, not overwriting: {dst}')
+            shutil.move(src, dst)
+            setattr(self, attr, os.fspath(dst))
diff --git a/test/data/variational/eta_should_be_big_vb.csv b/test/data/variational/eta_should_be_big_vb.csv
new file mode 100644
index 00000000..8034922d
--- /dev/null
+++ b/test/data/variational/eta_should_be_big_vb.csv
@@ -0,0 +1,1039 @@
+# stan_version_major = 2
+# stan_version_minor = 37
+# stan_version_patch = 0
+# model = eta_should_be_big_model
+# start_datetime = 2026-05-03 16:33:45 UTC
+# method = variational
+#   variational
+#     algorithm = meanfield (Default)
+#       meanfield
+#     iter = 10000 (Default)
+#     grad_samples = 1 (Default)
+#     elbo_samples = 100 (Default)
+#     eta = 1 (Default)
+#     adapt
+#       engaged = true (Default)
+#       iter = 50 (Default)
+#     tol_rel_obj = 0.01 (Default)
+#     eval_elbo = 100 (Default)
+#     output_samples = 1000 (Default)
+# id = 1 (Default)
+# data
+#   file =  (Default)
+# init = 2 (Default)
+# random
+#   seed = 999999
+# output
+#   file = /tmp/tmpmw9luosp/eta_should_be_big0j7mxqbi/eta_should_be_big-20260503123345.csv
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+#   sig_figs = 8 (Default)
+#   profile_file = profile.csv (Default)
+#   save_cmdstan_config = true
+# num_threads = 1 (Default)
+# stanc_version = stanc3 v2.37.0
+# stancflags = --filename-in-msg=eta_should_be_big.stan
+lp__,log_p__,log_g__,mu.1,mu.2
+# Stepsize adaptation complete.
+# eta = 10
+0,0,0,302.14243,361.00516
+0,-180254.01,-0.34841061,1171.45,360.87331
+0,-193632.42,-2.1336959,-754.93113,357.99026
+0,-190332.32,-0.16356062,-295.91242,360.97842
+0,-189196.65,-0.20674205,-134.60856,360.18683
+0,-180094.25,-0.73392397,1194.3343,362.445
+0,-181949.94,-0.31936691,917.06328,361.91119
+0,-178889.76,-1.0641511,1378.7898,359.27508
+0,-191176.56,-0.47263933,-415.51839,362.15794
+0,-184317.77,-0.082847975,567.95369,361.53728
+0,-189355.45,-1.3143658,-155.90826,358.39294
+0,-179878.57,-0.52508719,1227.2545,361.8715
+0,-199686.9,-2.2108644,-1579.4451,359.18263
+0,-180228.72,-0.35034307,1175.0756,361.1149
+0,-181222.98,-0.83714291,1027.4247,359.17704
+0,-189540.69,-0.12789689,-183.7595,360.67038
+0,-188491.8,-2.1523642,-32.455824,357.57485
+0,-184424.79,-0.97960317,554.32189,358.69796
+0,-192177.13,-1.5237494,-552.99519,358.42409
+0,-186807.64,-0.037484148,206.93791,360.57305
+0,-190239.84,-0.17054926,-282.68672,360.72252
+0,-180913.65,-1.2085868,1070.68,363.29793
+0,-181764.01,-0.20399165,945.17798,361.29547
+0,-188001.03,-0.2913469,36.102946,359.80081
+0,-185986.78,-0.050439385,325.62615,360.47501
+0,-189455.87,-0.19304798,-171.47297,360.29305
+0,-187451.41,-2.7920331,111.26212,364.94755
+0,-182505.06,-0.23615381,834.76496,361.77753
+0,-176335.39,-1.2169009,1767.929,362.15272
+0,-179529.38,-0.59374147,1279.9194,361.94249
+0,-183786.78,-0.52543707,647.38618,359.38112
+0,-195602.8,-1.1028852,-1027.2228,359.71902
+0,-187741.78,-0.21909107,71.649552,362.04966
+0,-191548.51,-0.39478259,-467.44954,361.83898
+0,-200900.19,-2.2549385,-1741.5374,359.61221
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+0,-190686.59,-1.332476,-347.59416,363.53137
+0,-190776.61,-0.19986313,-358.55544,361.05247
+0,-175587.23,-1.1470263,1884.6606,361.12781
+0,-201289.9,-2.00931,-1794.138,360.91841
+0,-183881.73,-3.234643,629.23889,365.22897
+0,-172212.1,-2.0447647,2415.9624,360.87432
+0,-182101.48,-0.22764533,894.75319,361.6185
+0,-180360.6,-1.6943334,1153.2945,363.76824
+0,-194813.7,-0.70029146,-919.65776,360.68612
+0,-180477.66,-0.3289661,1137.5657,361.24244
+0,-189250.83,-0.13003293,-142.54438,360.53379
+0,-182749.67,-2.88326,796.26121,364.94493
+0,-169089.02,-4.103241,2921.4692,358.67658
+0,-196526.66,-2.0916504,-1152.4962,358.4954
+0,-185824.89,-0.75025286,350.17083,358.95684
+0,-183661.02,-0.06070334,664.65997,361.06563
+0,-186019.23,-0.0095897907,320.3867,361.23506
+0,-191708.17,-1.0857496,-487.77869,358.8877
+0,-191038.7,-0.22606596,-395.46816,361.14895
+0,-190354.66,-0.18272154,-298.86458,360.69058
+0,-186376.5,-0.74479428,267.41595,363.04669
+0,-184819.75,-0.035082428,495.1788,360.68718
+0,-187732.72,-0.51085607,72.522444,362.6562
+0,-183304.01,-0.084982361,717.39551,360.81897
+0,-184663.75,-0.51788923,518.92303,359.33783
+0,-183172.07,-0.09138373,736.59336,361.17478
+0,-184336.87,-0.087895098,565.13736,361.56661
+0,-183033.44,-0.097411943,757.08421,361.13124
+0,-172324.64,-2.2201148,2398.7819,359.91695
+0,-176285.3,-1.820434,1774.9103,363.16021
+0,-188913.13,-0.91680493,-96.386012,363.17951
+0,-194668.74,-0.67832611,-899.72602,360.68517
+0,-191407.49,-1.2924539,-445.46611,358.59525
+0,-176463.74,-1.2151343,1749.8729,359.80449
+0,-189507.21,-3.5845904,-176.18411,356.59057
+0,-188670.26,-1.5717949,-62.320177,363.91417
+0,-186743.03,-0.5446608,214.719,362.74602
+0,-186611.56,-0.066255209,235.33511,360.40548
diff --git a/test/data/variational/eta_should_be_big_vb_config.json b/test/data/variational/eta_should_be_big_vb_config.json
new file mode 100644
index 00000000..d85f9c64
--- /dev/null
+++ b/test/data/variational/eta_should_be_big_vb_config.json
@@ -0,0 +1,49 @@
+
+{
+  "stan_major_version" : "2",
+  "stan_minor_version" : "37",
+  "stan_patch_version" : "0",
+  "model_name" : "eta_should_be_big_model",
+  "start_datetime" : "2026-05-03 16:33:45 UTC",
+  "method" : {
+    "value" : "variational",
+    "variational" : {
+      "algorithm" : {
+        "value" : "meanfield",
+        "meanfield" : {
+        }
+      },
+      "iter" : 10000,
+      "grad_samples" : 1,
+      "elbo_samples" : 100,
+      "eta" : 1,
+      "adapt" : {
+        "engaged" : true,
+        "iter" : 50
+      },
+      "tol_rel_obj" : 0.01,
+      "eval_elbo" : 100,
+      "output_samples" : 1000
+    }
+  },
+  "id" : 1,
+  "data" : {
+    "file" : ""
+  },
+  "init" : "2",
+  "random" : {
+    "seed" : 999999
+  },
+  "output" : {
+    "file" : "\/tmp\/tmpmw9luosp\/eta_should_be_big0j7mxqbi\/eta_should_be_big-20260503123345.csv",
+    "diagnostic_file" : "",
+    "refresh" : 100,
+    "sig_figs" : 8,
+    "profile_file" : "profile.csv",
+    "save_cmdstan_config" : true
+  },
+  "num_threads" : 1,
+  "mpi_enabled" : false,
+  "stanc_version" : "stanc3 v2.37.0",
+  "stancflags" : "--filename-in-msg=eta_should_be_big.stan"
+}
diff --git a/test/test_compliance.py b/test/test_compliance.py
index 1b30c568..5e9ef431 100644
--- a/test/test_compliance.py
+++ b/test/test_compliance.py
@@ -48,8 +48,8 @@ def test_optimize_copy_ability() -> None:
 
 
 def test_variational_pickle_ability() -> None:
-    csvfiles_path = DATAFILES_PATH / 'variational'
-    fit = cmdstanpy.from_csv(path=csvfiles_path)
+    csv_path = DATAFILES_PATH / 'variational' / 'eta_should_be_big_vb.csv'
+    fit = cmdstanpy.from_csv(path=csv_path)
     assert fit is not None
     keys = fit.stan_variables().keys()
     pickled = pickle.dumps(fit)
@@ -59,8 +59,8 @@ def test_variational_pickle_ability() -> None:
 
 
 def test_variational_copy_ability() -> None:
-    csvfiles_path = DATAFILES_PATH / 'variational'
-    fit = cmdstanpy.from_csv(path=csvfiles_path)
+    csv_path = DATAFILES_PATH / 'variational' / 'eta_should_be_big_vb.csv'
+    fit = cmdstanpy.from_csv(path=csv_path)
     assert fit is not None
     fit2 = copy.deepcopy(fit)
     assert fit.stan_variables().keys() == fit2.stan_variables().keys()
diff --git a/test/test_variational.py b/test/test_variational.py
index edc6db4d..2471092e 100644
--- a/test/test_variational.py
+++ b/test/test_variational.py
@@ -8,60 +8,23 @@
 import shutil
 from math import fabs
 from test import check_present
-from typing import Any
 
 import numpy as np
 import pytest
 
-from cmdstanpy.cmdstan_args import CmdStanArgs, VariationalArgs
 from cmdstanpy.model import CmdStanModel
-from cmdstanpy.stanfit import CmdStanVB, RunSet, from_csv
+from cmdstanpy.stanfit import CmdStanVB, from_csv
 from cmdstanpy.utils.cmdstan import cmdstan_version_before
 
 HERE = os.path.dirname(os.path.abspath(__file__))
 DATAFILES_PATH = os.path.join(HERE, 'data')
 
 
-def test_instantiate() -> None:
-    stan = os.path.join(DATAFILES_PATH, 'variational', 'eta_should_be_big.stan')
-    model = CmdStanModel(stan_file=stan)
-    no_data: dict[str, Any] = {}
-    args = VariationalArgs(algorithm='meanfield')
-    cmdstan_args = CmdStanArgs(
-        model_name=model.name,
-        model_exe=model.exe_file,
-        chain_ids=None,
-        data=no_data,
-        method_args=args,
-    )
-    runset = RunSet(args=cmdstan_args, chains=1)
-    runset._csv_files = [
-        os.path.join(DATAFILES_PATH, 'variational', 'eta_big_output.csv')
-    ]
-    variational = CmdStanVB(runset)
-    assert 'CmdStanVB: model=eta_should_be_big' in repr(variational)
-    assert 'method=variational' in repr(variational)
-    assert variational.column_names == (
-        'lp__',
-        'log_p__',
-        'log_g__',
-        'mu[1]',
-        'mu[2]',
-    )
-    assert variational.eta == 100
-
-    np.testing.assert_almost_equal(
-        variational.variational_params_dict['mu[1]'], 311.545, decimal=2
-    )
-    np.testing.assert_almost_equal(
-        variational.variational_params_dict['mu[2]'], 532.801, decimal=2
+def test_instantiate_from_csv() -> None:
+    csv_path = os.path.join(
+        DATAFILES_PATH, 'variational', 'eta_should_be_big_vb.csv'
     )
-    assert variational.variational_sample.shape == (1000, 5)
-
-
-def test_instantiate_from_csvfiles() -> None:
-    csvfiles_path = os.path.join(DATAFILES_PATH, 'variational')
-    variational = from_csv(path=csvfiles_path)
+    variational = from_csv(csv_path)
     assert isinstance(variational, CmdStanVB)
     assert 'CmdStanVB: model=eta_should_be_big' in repr(variational)
     assert 'method=variational' in repr(variational)
@@ -72,14 +35,6 @@ def test_instantiate_from_csvfiles() -> None:
         'mu[1]',
         'mu[2]',
     )
-    assert variational.eta == 100
-
-    np.testing.assert_almost_equal(
-        variational.variational_params_dict['mu[1]'], 311.545, decimal=2
-    )
-    np.testing.assert_almost_equal(
-        variational.variational_params_dict['mu[2]'], 532.801, decimal=2
-    )
     assert variational.variational_sample.shape == (1000, 5)
 
 
@@ -335,7 +290,7 @@ def test_serialization() -> None:
     model = CmdStanModel(stan_file=stan)
     variational1 = model.variational(algorithm='meanfield', seed=999999)
     dumped = pickle.dumps(variational1)
-    shutil.rmtree(variational1.runset._outdir)
+    shutil.rmtree(os.path.dirname(variational1.csv_file))
     variational2: CmdStanVB = pickle.loads(dumped)
     np.testing.assert_array_equal(
         variational1.variational_sample, variational2.variational_sample