diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 00000000..99afe5e1
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,39 @@
+name: 🐛Bug Report
+description: File a bug report here
+title: "[BUG]: "
+labels: ["bug"]
+assignees: [""]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Thanks for taking the time to fill out this bug report 🤗
+ Make sure there aren't any open/closed issues for this topic 😃
+
+ - type: textarea
+ id: bug-description
+ attributes:
+ label: Description of the bug
+ description: Give us a brief description of what happened and what should have happened
+ validations:
+ required: true
+
+ - type: textarea
+ id: steps-to-reproduce
+ attributes:
+ label: Steps To Reproduce
+ description: Steps to reproduce the behavior.
+ placeholder: |
+ 1. Go to '...'
+ 2. Click on '...'
+ 3. Scroll down to '...'
+ 4. See error
+ validations:
+ required: true
+
+ - type: textarea
+ id: additional-information
+ attributes:
+ label: Additional Information
+ description: |
+ Provide any additional information such as logs, screenshots, likes, scenarios in which the bug occurs so that it facilitates resolving the issue.
diff --git a/.github/ISSUE_TEMPLATE/codebase_reorg.yml b/.github/ISSUE_TEMPLATE/codebase_reorg.yml
new file mode 100644
index 00000000..4606f981
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/codebase_reorg.yml
@@ -0,0 +1,24 @@
+name: 🧹Codebase Refactor
+description: Refactor, clean, format the codebase
+title: "[ORG]: "
+labels: ["refactor"]
+assignees: [""]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Please make sure this codebase refactor request hasn't been already submitted by someone by looking through other open/closed issues
+
+ - type: textarea
+ id: description
+ attributes:
+ label: Description
+ description: Give us a brief description of the codebase refactor task you would like
+ validations:
+ required: true
+
+ - type: textarea
+ id: additional-information
+ attributes:
+ label: Additional Information
+ description: Give us some additional reason on why codebase refactor is necessary to do
diff --git a/.github/ISSUE_TEMPLATE/exp_record.yml b/.github/ISSUE_TEMPLATE/exp_record.yml
new file mode 100644
index 00000000..f71dea1c
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/exp_record.yml
@@ -0,0 +1,24 @@
+name: 🧪Experiment Record
+description: Describe experiment setting and results here
+title: "[EXP]: "
+labels: ["experiment"]
+assignees: [""]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Please make sure this experiment request hasn't been already submitted by someone by looking through other open/closed issues
+
+ - type: textarea
+ id: description
+ attributes:
+ label: Description
+ description: Give us a brief description of the experimental setting and results you would like
+ validations:
+ required: true
+
+ - type: textarea
+ id: additional-information
+ attributes:
+ label: Additional Information
+ description: Give us some additional information on the experimental setting and results like learning rate, data selection , etc.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 00000000..40dc70e3
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,23 @@
+name: ✨Feature Request
+description: Request a new feature or enhancement
+labels: ["enhancement"]
+title: "[FEAT]: "
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Please make sure this feature request hasn't been already submitted by someone by looking through other open/closed issues
+
+ - type: textarea
+ id: description
+ attributes:
+ label: Description
+ description: Give us a brief description of the feature or enhancement you would like
+ validations:
+ required: true
+
+ - type: textarea
+ id: additional-information
+ attributes:
+ label: Additional Information
+ description: Give us some additional information on the feature request like proposed solutions, links, screenshots, etc.
diff --git a/.github/ISSUE_TEMPLATE/writing_task.yml b/.github/ISSUE_TEMPLATE/writing_task.yml
new file mode 100644
index 00000000..502f20b6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/writing_task.yml
@@ -0,0 +1,24 @@
+name: 🖊️Writing Task
+description: Describe writing task here
+title: "[WRT]: "
+labels: ["writing"]
+assignees: [""]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Please make sure this writing task request hasn't been already submitted by someone by looking through other open/closed issues
+
+ - type: textarea
+ id: description
+ attributes:
+ label: Description
+ description: Give us a brief description of the writing task you would like
+ validations:
+ required: true
+
+ - type: textarea
+ id: additional-information
+ attributes:
+ label: Additional Information
+ description: Give us some additional information on the writing task like exptected length, main content, etc.
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 00000000..2775ad7e
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+ - package-ecosystem: "pip" # See documentation for possible values
+ directory: "/" # Location of package manifests
+ schedule:
+ interval: "daily"
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 00000000..af460a9c
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,27 @@
+
+
+
+Closes #
+
+## 📑 Description
+
+
+
+
+## ✅ Checks
+
+- [ ] My pull request adheres to the code style of this project
+- [ ] My code requires changes to the documentation
+- [ ] I have updated the documentation as required
+- [ ] All the tests have passed
+- [ ] Branch name follows `type/descript` (e.g. `feature/add-llm-agents`)
+- [ ] Ready for code review
+
+## ℹ Additional Information
+
diff --git a/.github/workflows/codespell.yml b/.github/workflows/codespell.yml
new file mode 100644
index 00000000..d063beff
--- /dev/null
+++ b/.github/workflows/codespell.yml
@@ -0,0 +1,31 @@
+name: codespell
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+jobs:
+ codespell:
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ python-version: ["3.10"]
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install codespell==2.2.6 tomli==2.0.1
+ - name: Spelling check with codespell
+ run: |
+ codespell -c pyproject.toml
diff --git a/.github/workflows/isort.yml b/.github/workflows/isort.yml
new file mode 100644
index 00000000..2229a390
--- /dev/null
+++ b/.github/workflows/isort.yml
@@ -0,0 +1,31 @@
+name: isort
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+jobs:
+ isort:
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ python-version: ["3.10"]
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install isort==5.13.2
+ - name: Run isort
+ run: |
+ isort . --check-only
diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml
new file mode 100644
index 00000000..da9c2c71
--- /dev/null
+++ b/.github/workflows/mypy.yml
@@ -0,0 +1,35 @@
+name: Mypy
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+jobs:
+ Static-Type-Checking:
+ runs-on: ubuntu-latest
+ strategy:
+ max-parallel: 5
+ matrix:
+ python-version: ["3.10"]
+
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ curl -sSL https://install.python-poetry.org | python3
+ poetry install --all-extras
+ - name: Type-checking package with mypy
+ run: |
+ # Run this mypy instance against our main package.
+ poetry run pip install types-protobuf==4.24.0.4
+ poetry run mypy --config-file pyproject.toml .
diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
new file mode 100644
index 00000000..96564e62
--- /dev/null
+++ b/.github/workflows/pytest.yml
@@ -0,0 +1,36 @@
+name: Pytest
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+jobs:
+ Pytest:
+ runs-on: ubuntu-latest
+ strategy:
+ max-parallel: 5
+ matrix:
+ python-version: ["3.10"]
+
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ curl -sSL https://install.python-poetry.org | python3
+ poetry install --all-extras
+ - name: Test with pytest
+ env:
+ OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+ TOGETHERAI_API_KEY: ${{ secrets.TOGETHERAI_API_KEY }}
+ run: |
+ poetry run pytest
diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
new file mode 100644
index 00000000..8ae4809a
--- /dev/null
+++ b/.github/workflows/ruff.yml
@@ -0,0 +1,34 @@
+name: ruff
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+jobs:
+ ruff:
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ python-version: ["3.10"]
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install ruff==0.5.1
+ - name: Analysing the code with ruff
+ run: |
+ ruff check .
+ - name: Format the code with ruff
+ run: |
+ ruff format .
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..e69de29b
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..cd1d13d0
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,28 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v3.2.0
+ hooks:
+ - id: trailing-whitespace
+ - id: end-of-file-fixer
+ - id: check-yaml
+ - id: check-added-large-files
+- repo: https://github.com/pre-commit/mirrors-prettier
+ rev: v3.0.1 # Use the sha / tag you want to point at
+ hooks:
+ - id: prettier
+ types_or: [html]
+- repo: https://github.com/astral-sh/ruff-pre-commit
+ rev: v0.3.5 # Ruff version
+ hooks:
+ - id: ruff
+ types_or: [python, pyi, jupyter]
+ args: [--fix]
+
+- repo: https://github.com/pre-commit/mirrors-isort
+ rev: v5.10.1 # Use the latest isort version
+ hooks:
+ - id: isort # This will sort imports automatically
+- repo: https://github.com/kynan/nbstripout
+ rev: 0.6.0
+ hooks:
+ - id: nbstripout
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 00000000..261eeb9e
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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diff --git a/Readme.md b/Readme.md
new file mode 100644
index 00000000..c9ed05cb
--- /dev/null
+++ b/Readme.md
@@ -0,0 +1,382 @@
+# OpenManus-RL
+🤗 Dataset (OpenManus-RL)
+
+OpenManus-RL is an open-source initiative collaboratively led by __Ulab-UIUC__ and __MetaGPT__ .
+
+This project is an extended version of the original [@OpenManus](https://github.com/mannaandpoem/OpenManus) initiative. Inspired by successful RL tunning for reasoning LLM such as Deepseek-R1, QwQ-32B, we will explore new paradigms for RL-based LLM agent tuning, particularly building upon foundations.
+
+We are committed to regularly updating our exploration directions and results in a dynamic, live-streaming fashion. All progress, including rigorous testing on agent benchmarks such as GAIA, AgentBench, WebShop, and OSWorld, and tuned models, will be openly shared and continuously updated.
+
+We warmly welcome contributions from the broader community—join us in pushing the boundaries of agent reasoning and tool integration!
+
+Code and dataset coming soon! Stay tuned!
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Reasoning-Acting integration
+- 🧠 **Structured Training** - Separate format/reasoning learning
+- 🚫 **Anti-Hallucination** - Negative samples + environment grounding
+- 🌐 **6 Domains** - OS, DB, Web, KG, Household, E-commerce
+
+### Dataset Overview
+| Source | Trajectories | Avg Turns | Key Features |
+|--------|--------------|-----------|--------------|
+| [AgentInstruct](https://huggingface.co/datasets/THUDM/AgentInstruct) | 1,866 | 5.24 | Multi-task QA, CoT reasoning |
+| [Agent-FLAN](https://huggingface.co/datasets/internlm/Agent-FLAN) | 34442 | 3-35 | Error recovery patterns, diverse real-world tasks|
+ [AgentTraj-L](https://huggingface.co/datasets/AgentGym/AgentTraj-L) | 14485 | 3-35 | interactive environments and tasks
+| **Combined** | 50793 | 4-20 | Enhanced generalization, uniform format with broader task coverage |
+
+### Supported Tasks
+- **text-generation**: ReAct-style instruction following
+- **conversational-ai**: Tool-augmented dialogues
+
+### Languages
+English
+
+## Data Instances
+
+**ReAct Pattern Example**:
+```json
+{
+ "id": "os_0",
+ "conversations": [
+ {"role": "user", "content": "Count files in /etc"},
+ {"role": "assistant", "content": "Think: Need reliable counting method\nAct: bash\n```bash\nls -1 /etc | wc -l\n```"},
+ {"role": "user", "content": "OS Output: 220"},
+ {"role": "assistant", "content": "Think: Verified through execution\nAct: answer(220)"}
+ ]
+}
+```
+
+# Running
+
+## OpenManus-RL
+
+A simplified library for Supervised Fine-Tuning (SFT) and GRPO tunning of language models for agentic system. (developed upon [Open-R1](https://github.com/huggingface/open-r1) from huggingface)
+We are still laboriously developing this part, welcome feedback.
+
+## Installation
+
+First, create a conda environment and activate it:
+
+```bash
+conda create -n openmanus-rl python=3.10
+conda activate openmanus-rl
+```
+
+Then, install the required dependencies:
+
+
+```bash
+pip install -r requirements.txt
+```
+
+Supervised Fine-Tuning (SFT)
+
+Basic Usage
+
+To fine-tune a model on a single GPU:
+
+
+```bash
+python -m openmanus_rl.sft \
+ --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+ --dataset_name CharlieDreemur/OpenManus-RL \
+ --learning_rate 2.0e-5 \
+ --num_train_epochs 1 \
+ --packing \
+ --max_seq_length 4096 \
+ --per_device_train_batch_size 2 \
+ --gradient_accumulation_steps 8 \
+ --gradient_checkpointing \
+ --bf16 \
+ --logging_steps 5 \
+ --output_dir data/sft-output
+```
+
+Distributed Training with Accelerate
+
+For multi-GPU training using Accelerate:
+
+
+```bash
+accelerate launch --config_file=configs/accelerate_configs/zero3.yaml openmanus_rl/sft.py \
+ --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+ --dataset_name CharlieDreemur/OpenManus-RL \
+ --learning_rate 2.0e-5 \
+ --num_train_epochs 1 \
+ --packing \
+ --max_seq_length 4096 \
+ --per_device_train_batch_size 2 \
+ --gradient_accumulation_steps 8 \
+ --gradient_checkpointing \
+ --bf16 \
+ --logging_steps 5 \
+ --output_dir data/sft-output
+```
+
+## Gradient-based Reinforcement for Policy Optimization (GRPO) for agent tunning
+Basic Usage
+To fine-tune a model using GRPO on a single GPU:
+
+```bash
+python -m openmanus_rl.grpo \
+ --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+ --dataset_name CharlieDreemur/OpenManus-RL-GRPO \
+ --learning_rate 2.0e-5 \
+ --num_train_epochs 1 \
+ --max_seq_length 4096 \
+ --per_device_train_batch_size 2 \
+ --gradient_accumulation_steps 8 \
+ --gradient_checkpointing \
+ --bf16 \
+ --reward_funcs accuracy format tag_count \
+ --logging_steps 5 \
+ --output_dir data/grpo-output
+```
+Distributed Training with Accelerate
+For multi-GPU training using Accelerate:
+
+```bash
+accelerate launch --config_file=configs/accelerate_configs/zero3.yaml openmanus_rl/grpo.py \
+ --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+ --dataset_name CharlieDreemur/OpenManus-RL-GRPO \
+ --learning_rate 2.0e-5 \
+ --num_train_epochs 1 \
+ --max_seq_length 4096 \
+ --per_device_train_batch_size 2 \
+ --gradient_accumulation_steps 8 \
+ --gradient_checkpointing \
+ --bf16 \
+ --reward_funcs accuracy format tag_count \
+ --logging_steps 5 \
+ --output_dir data/grpo-output
+```
+
+
+# Related Work
+
+## Agent tuning
+
+1. **Offline Training of Language Model Agents with Functions as Learnable Weights**. [[paper](https://arxiv.org/pdf/2402.11359)]
+2. **FIREACT : TOWARD LANGUAGE AGENT FINE-TUNING**. [[paper](https://arxiv.org/pdf/2310.05915)]
+3. **AgentTuning: Enabling Generalized Agent Abilities for LLMs**. [[paper](https://arxiv.org/pdf/2310.12823)]
+4. **ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy**. [[paper](https://arxiv.org/pdf/2403.14589)]
+5. **UI-TARS: Pioneering Automated GUI Interaction with Native Agents**. [[paper](https://arxiv.org/pdf/2501.12326#page=16.83)]
+6. **ATLAS: Agent Tuning via Learning Critical Steps**. [[paper](https://arxiv.org/pdf/2503.02197)]
+
+## Tool using
+
+1. **Toolformer: Language Models Can Teach Themselves to Use Tools**. [[paper](https://arxiv.org/pdf/2302.04761)]
+2. **ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs**. [[paper](https://arxiv.org/abs/2307.16789)]
+
+## Agent tuning instruction dataset
+
+1. **Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models**. [[paper](https://arxiv.org/pdf/2403.12881)]
+2. **AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning**. [[paper](https://arxiv.org/pdf/2402.15506)]
+
+## RL tuning
+
+1. **Training Language Models to Follow Instructions with Human Feedback**. [[paper](https://arxiv.org/pdf/2305.18438)]
+2. **Deepseekmath: Pushing the Limits of Mathematical Reasoning in Open Language Models**. [[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf)]
+3. **DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning**. [[paper](https://arxiv.org/pdf/2501.12948)]
+
+## **Benchmark:**
+
+1. **AgentBench: Evaluating LLMs as Agents**. [paper](https://arxiv.org/abs/2308.03688)
+2. **OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments**. [paper](https://arxiv.org/abs/2404.07972)
+3. **AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents**. [paper](https://openreview.net/forum?id=il5yUQsrjC)
+4. **WebShop: Towards Scalable Real-World Web Interaction with Autonomous Agents**. [paper](https://arxiv.org/pdf/2207.01206)
+5. **GAIA: a benchmark for General AI Assistants**. [paper](https://arxiv.org/abs/2311.12983)
+6. **TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks**. [paper](https://arxiv.org/abs/2412.14161)
+
+
+## Similar Code
+
+1. **RAGEN: Training Agents by Reinforcing Reasoning**. [[code](https://github.com/ZihanWang314/RAGEN)]
+
+# Acknowledgement
+We extend our thanks to ulab-uiuc (https://ulab-uiuc.github.io/) and Openmanus (https://github.com/mannaandpoem/OpenManus)) team from MetaGPT for their support and shared knowledge. Their mission and community contributions help drive innovations like OpenManus forward.
+We welcome all developers who are interested in this project can reach out to (kunlunz2@illinois.edu)
+
+Stay tuned for updates and the official release of our repository. Together, let's build a thriving open-source agent ecosystem!
+
+# Community Group
+
+Join our networking group on Feishu and share your experience with other developers!
+
+
+
+
+
+
+
+
+
diff --git a/SECURITY.md b/SECURITY.md
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+# Security Policy
+
+## Supported Versions
+
+Use this section to tell people about which versions of your project are
+currently being supported with security updates.
+
+| Version | Supported |
+| ------- | ------------------ |
+| 5.1.x | :white_check_mark: |
+| 5.0.x | :x: |
+| 4.0.x | :white_check_mark: |
+| < 4.0 | :x: |
+
+## Reporting a Vulnerability
+
+Use this section to tell people how to report a vulnerability.
+
+Tell them where to go, how often they can expect to get an update on a
+reported vulnerability, what to expect if the vulnerability is accepted or
+declined, etc.
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+# coding=utf-8
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+from typing import Optional
+
+import trl
+
+
+# TODO: add the shared options with a mixin to reduce code duplication
+@dataclass
+class GRPOConfig(trl.GRPOConfig):
+ """
+ args for callbacks, benchmarks etc
+ """
+
+ benchmarks: list[str] = field(
+ default_factory=lambda: [], metadata={"help": "The benchmarks to run after training."}
+ )
+ callbacks: list[str] = field(
+ default_factory=lambda: [], metadata={"help": "The callbacks to run during training."}
+ )
+ chat_template: Optional[str] = field(default=None, metadata={"help": "The chat template to use."})
+ system_prompt: Optional[str] = field(
+ default=None,
+ metadata={"help": "The optional system prompt to use."},
+ )
+ hub_model_revision: Optional[str] = field(
+ default="main", metadata={"help": "The Hub model branch to push the model to."}
+ )
+ overwrite_hub_revision: bool = field(default=False, metadata={"help": "Whether to overwrite the Hub revision."})
+ push_to_hub_revision: bool = field(default=False, metadata={"help": "Whether to push to a Hub revision/branch."})
+ wandb_entity: Optional[str] = field(
+ default=None,
+ metadata={"help": ("The entity to store runs under.")},
+ )
+ wandb_project: Optional[str] = field(
+ default=None,
+ metadata={"help": ("The project to store runs under.")},
+ )
+
+
+@dataclass
+class SFTConfig(trl.SFTConfig):
+ """
+ args for callbacks, benchmarks etc
+ """
+
+ benchmarks: list[str] = field(
+ default_factory=lambda: [], metadata={"help": "The benchmarks to run after training."}
+ )
+ callbacks: list[str] = field(
+ default_factory=lambda: [], metadata={"help": "The callbacks to run during training."}
+ )
+ chat_template: Optional[str] = field(default=None, metadata={"help": "The chat template to use."})
+ system_prompt: Optional[str] = field(
+ default=None,
+ metadata={"help": "The optional system prompt to use for benchmarking."},
+ )
+ hub_model_revision: Optional[str] = field(
+ default="main",
+ metadata={"help": "The Hub model branch to push the model to."},
+ )
+ overwrite_hub_revision: bool = field(default=False, metadata={"help": "Whether to overwrite the Hub revision."})
+ push_to_hub_revision: bool = field(default=False, metadata={"help": "Whether to push to a Hub revision/branch."})
+ wandb_entity: Optional[str] = field(
+ default=None,
+ metadata={"help": ("The entity to store runs under.")},
+ )
+ wandb_project: Optional[str] = field(
+ default=None,
+ metadata={"help": ("The project to store runs under.")},
+ )
diff --git a/openmanus_rl/grpo.py b/openmanus_rl/grpo.py
new file mode 100644
index 00000000..eda36d8d
--- /dev/null
+++ b/openmanus_rl/grpo.py
@@ -0,0 +1,271 @@
+# Copyright 2025 The OpenManus-RL Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Simplified Gradient-based Reinforcement for Policy Optimization (GRPO) script for language models.
+
+Usage:
+# Fine-tune a model on a single GPU
+python -m openmanus_rl.grpo \
+ --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+ --dataset_name HuggingFaceH4/Bespoke-Stratos-17k \
+ --learning_rate 2.0e-5 \
+ --num_train_epochs 1 \
+ --max_seq_length 4096 \
+ --per_device_train_batch_size 2 \
+ --gradient_accumulation_steps 8 \
+ --gradient_checkpointing \
+ --bf16 \
+ --logging_steps 5 \
+ --output_dir data/grpo-output
+"""
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+
+import datasets
+import torch
+import transformers
+from datasets import load_dataset
+from transformers import set_seed
+from transformers.trainer_utils import get_last_checkpoint
+from trl import GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config
+
+from .configs import GRPOConfig
+from .rewards import (
+ accuracy_reward,
+ format_reward,
+ get_cosine_scaled_reward,
+ get_repetition_penalty_reward,
+ get_trajectories_format_reward,
+ len_reward,
+ reasoning_steps_reward,
+ tag_count_reward,
+)
+from .utils import get_tokenizer
+
+logger = logging.getLogger(__name__)
+
+
+def init_wandb_training(training_args):
+ """
+ Helper function for setting up Weights & Biases logging tools.
+ """
+ if training_args.wandb_entity is not None:
+ os.environ["WANDB_ENTITY"] = training_args.wandb_entity
+ if training_args.wandb_project is not None:
+ os.environ["WANDB_PROJECT"] = training_args.wandb_project
+
+
+@dataclass
+class GRPOScriptArguments(ScriptArguments):
+ """
+ Script arguments for the GRPO training script.
+
+ Args:
+ reward_funcs (`list[str]`):
+ List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length', 'tag_count', 'trajectories_format'.
+ cosine_min_value_wrong (`float`):
+ Minimum reward for cosine scaling for wrong answers.
+ cosine_max_value_wrong (`float`):
+ Maximum reward for cosine scaling for wrong answers.
+ cosine_min_value_correct (`float`):
+ Minimum reward for cosine scaling for correct answers.
+ cosine_max_value_correct (`float`):
+ Maximum reward for cosine scaling for correct answers.
+ cosine_max_len (`int`):
+ Maximum length for cosine scaling.
+ """
+
+ reward_funcs: list[str] = field(
+ default_factory=lambda: ["accuracy", "format", "tag_count"],
+ metadata={
+ "help": "List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length', tag_count', 'trajectories_format'"
+ },
+ )
+ cosine_min_value_wrong: float = field(
+ default=0.0,
+ metadata={"help": "Minimum reward for wrong answers"},
+ )
+ cosine_max_value_wrong: float = field(
+ default=-0.5,
+ metadata={"help": "Maximum reward for wrong answers"},
+ )
+ cosine_min_value_correct: float = field(
+ default=0.5,
+ metadata={"help": "Minimum reward for correct answers"},
+ )
+ cosine_max_value_correct: float = field(
+ default=1.0,
+ metadata={"help": "Maximum reward for correct answers"},
+ )
+ cosine_max_len: int = field(
+ default=1000,
+ metadata={"help": "Maximum length for scaling"},
+ )
+ repetition_n_grams: int = field(
+ default=3,
+ metadata={"help": "Number of n-grams for repetition penalty reward"},
+ )
+ repetition_max_penalty: float = field(
+ default=-1.0,
+ metadata={"help": "Maximum (negative) penalty for for repetition penalty reward"},
+ )
+
+
+def main(script_args, training_args, model_args):
+ # Set seed for reproducibility
+ set_seed(training_args.seed)
+
+ ###############
+ # Setup logging
+ ###############
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%Y-%m-%d %H:%M:%S",
+ handlers=[logging.StreamHandler(sys.stdout)],
+ )
+ log_level = training_args.get_process_log_level()
+ logger.setLevel(log_level)
+ datasets.utils.logging.set_verbosity(log_level)
+ transformers.utils.logging.set_verbosity(log_level)
+ transformers.utils.logging.enable_default_handler()
+ transformers.utils.logging.enable_explicit_format()
+
+ # Log on each process a small summary
+ logger.warning(
+ f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+ )
+ logger.info(f"Model parameters {model_args}")
+ logger.info(f"Script parameters {script_args}")
+ logger.info(f"Training parameters {training_args}")
+
+ # Check for last checkpoint
+ last_checkpoint = None
+ if os.path.isdir(training_args.output_dir):
+ last_checkpoint = get_last_checkpoint(training_args.output_dir)
+ if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+ logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
+
+ if "wandb" in training_args.report_to:
+ init_wandb_training(training_args)
+
+ # Load the dataset
+ dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
+
+ ################
+ # Load tokenizer
+ ################
+ tokenizer = get_tokenizer(model_args, training_args)
+
+ # Get reward functions
+ REWARD_FUNCS_REGISTRY = {
+ "accuracy": accuracy_reward,
+ "format": format_reward,
+ "reasoning_steps": reasoning_steps_reward,
+ "cosine": get_cosine_scaled_reward(
+ min_value_wrong=script_args.cosine_min_value_wrong,
+ max_value_wrong=script_args.cosine_max_value_wrong,
+ min_value_correct=script_args.cosine_min_value_correct,
+ max_value_correct=script_args.cosine_max_value_correct,
+ max_len=script_args.cosine_max_len,
+ ),
+ "repetition_penalty": get_repetition_penalty_reward(
+ ngram_size=script_args.repetition_n_grams,
+ max_penalty=script_args.repetition_max_penalty,
+ ),
+ "length": len_reward,
+ "trajectories_format": get_trajectories_format_reward(),
+ "tag_count": tag_count_reward,
+ }
+ reward_funcs = [REWARD_FUNCS_REGISTRY[func] for func in script_args.reward_funcs]
+
+ # Format into conversation
+ def make_conversation(example):
+ prompt = []
+
+ if training_args.system_prompt is not None:
+ prompt.append({"role": "system", "content": training_args.system_prompt})
+
+ prompt.append({"role": "user", "content": example["problem"]})
+ return {"prompt": prompt}
+
+ dataset = dataset.map(make_conversation)
+
+ for split in dataset:
+ if "messages" in dataset[split].column_names:
+ dataset[split] = dataset[split].remove_columns("messages")
+
+ logger.info("*** Initializing model kwargs ***")
+ torch_dtype = (
+ model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
+ )
+ model_kwargs = dict(
+ revision=model_args.model_revision,
+ trust_remote_code=model_args.trust_remote_code,
+ attn_implementation=model_args.attn_implementation,
+ torch_dtype=torch_dtype,
+ use_cache=False if training_args.gradient_checkpointing else True,
+ )
+ training_args.model_init_kwargs = model_kwargs
+
+ #############################
+ # Initialize the GRPO trainer
+ #############################
+ trainer = GRPOTrainer(
+ model=model_args.model_name_or_path,
+ reward_funcs=reward_funcs,
+ args=training_args,
+ train_dataset=dataset[script_args.dataset_train_split],
+ eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
+ peft_config=get_peft_config(model_args),
+ processing_class=tokenizer,
+ )
+
+ ###############
+ # Training loop
+ ###############
+ logger.info("*** Train ***")
+ checkpoint = None
+ if training_args.resume_from_checkpoint is not None:
+ checkpoint = training_args.resume_from_checkpoint
+ elif last_checkpoint is not None:
+ checkpoint = last_checkpoint
+ train_result = trainer.train(resume_from_checkpoint=checkpoint)
+ metrics = train_result.metrics
+ metrics["train_samples"] = len(dataset[script_args.dataset_train_split])
+ trainer.log_metrics("train", metrics)
+ trainer.save_metrics("train", metrics)
+ trainer.save_state()
+
+ ##################################
+ # Save model
+ ##################################
+ logger.info("*** Save model ***")
+ trainer.save_model(training_args.output_dir)
+ logger.info(f"Model saved to {training_args.output_dir}")
+
+ # Restore k,v cache for fast inference
+ if trainer.accelerator.is_main_process:
+ trainer.model.config.use_cache = True
+ trainer.model.config.save_pretrained(training_args.output_dir)
+
+
+if __name__ == "__main__":
+ parser = TrlParser((GRPOScriptArguments, GRPOConfig, ModelConfig))
+ script_args, training_args, model_args = parser.parse_args_and_config()
+ main(script_args, training_args, model_args)
diff --git a/openmanus_rl/rewards.py b/openmanus_rl/rewards.py
new file mode 100644
index 00000000..065ef66b
--- /dev/null
+++ b/openmanus_rl/rewards.py
@@ -0,0 +1,514 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Reward functions for GRPO training."""
+
+import asyncio
+import json
+import math
+import re
+from typing import Dict
+
+from latex2sympy2_extended import NormalizationConfig
+from math_verify import LatexExtractionConfig, parse, verify
+
+from .utils import is_e2b_available
+
+if is_e2b_available():
+ from dotenv import load_dotenv
+ from e2b_code_interpreter import AsyncSandbox
+
+ load_dotenv()
+
+
+def accuracy_reward(completions, solution, **kwargs):
+ """Reward function that checks if the completion is the same as the ground truth."""
+ contents = [completion[0]["content"] for completion in completions]
+ rewards = []
+ for content, sol in zip(contents, solution):
+ gold_parsed = parse(
+ sol,
+ extraction_mode="first_match",
+ extraction_config=[LatexExtractionConfig()],
+ )
+ if len(gold_parsed) != 0:
+ # We require the answer to be provided in correct latex (no malformed operators)
+ answer_parsed = parse(
+ content,
+ extraction_config=[
+ LatexExtractionConfig(
+ normalization_config=NormalizationConfig(
+ nits=False,
+ malformed_operators=False,
+ basic_latex=True,
+ equations=True,
+ boxed="all",
+ units=True,
+ ),
+ # Ensures that boxed is tried first
+ boxed_match_priority=0,
+ try_extract_without_anchor=False,
+ )
+ ],
+ extraction_mode="first_match",
+ )
+ # Reward 1 if the content is the same as the ground truth, 0 otherwise
+ try:
+ reward = float(verify(answer_parsed, gold_parsed))
+ except Exception as e:
+ print(f"verify failed: {e}, answer: {answer_parsed}, gold: {gold_parsed}")
+ reward = 0.0
+ else:
+ # If the gold solution is not parseable, we reward 1 to skip this example
+ reward = 1.0
+ print("Failed to parse gold solution: ", sol)
+ rewards.append(reward)
+
+ return rewards
+
+
+def format_reward(completions, **kwargs):
+ """Reward function that checks if the reasoning process is enclosed within and tags, while the final answer is enclosed within and tags."""
+ pattern = r"^\n.*?\n \n\n.*?\n $"
+ completion_contents = [completion[0]["content"] for completion in completions]
+ matches = [re.match(pattern, content, re.DOTALL | re.MULTILINE) for content in completion_contents]
+ return [1.0 if match else 0.0 for match in matches]
+
+
+def tag_count_reward(completions, **kwargs) -> list[float]:
+ """Reward function that checks if we produce the desired number of think and answer tags associated with `format_reward()`.
+
+ Adapted from: https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb#file-grpo_demo-py-L90
+ """
+
+ def count_tags(text: str) -> float:
+ count = 0.0
+ if text.count("\n") == 1:
+ count += 0.25
+ if text.count("\n \n") == 1:
+ count += 0.25
+ if text.count("\n\n") == 1:
+ count += 0.25
+ if text.count("\n ") == 1:
+ count += 0.25
+ return count
+
+ contents = [completion[0]["content"] for completion in completions]
+ return [count_tags(c) for c in contents]
+
+
+def reasoning_steps_reward(completions, **kwargs):
+ r"""Reward function that checks for clear step-by-step reasoning.
+ Regex pattern:
+ Step \d+: - matches "Step 1:", "Step 2:", etc.
+ ^\d+\. - matches numbered lists like "1.", "2.", etc. at start of line
+ \n- - matches bullet points with hyphens
+ \n\* - matches bullet points with asterisks
+ First,|Second,|Next,|Finally, - matches transition words
+ """
+ pattern = r"(Step \d+:|^\d+\.|\n-|\n\*|First,|Second,|Next,|Finally,)"
+ completion_contents = [completion[0]["content"] for completion in completions]
+ matches = [len(re.findall(pattern, content)) for content in completion_contents]
+
+ # Magic number 3 to encourage 3 steps and more, otherwise partial reward
+ return [min(1.0, count / 3) for count in matches]
+
+
+def len_reward(completions: list[Dict[str, str]], solution: list[str], **kwargs) -> float:
+ """Compute length-based rewards to discourage overthinking and promote token efficiency.
+
+ Taken from the Kimi 1.5 tech report: https://arxiv.org/abs/2501.12599
+
+ Args:
+ completions: List of model completions
+ solution: List of ground truth solutions
+
+ Returns:
+ List of rewards where:
+ - For correct answers: reward = 0.5 - (len - min_len)/(max_len - min_len)
+ - For incorrect answers: reward = min(0, 0.5 - (len - min_len)/(max_len - min_len))
+ """
+ contents = [completion[0]["content"] for completion in completions]
+
+ # First check correctness of answers
+ correctness = []
+ for content, sol in zip(contents, solution):
+ gold_parsed = parse(
+ sol,
+ extraction_mode="first_match",
+ extraction_config=[LatexExtractionConfig()],
+ )
+ if len(gold_parsed) == 0:
+ # Skip unparseable examples
+ correctness.append(True) # Treat as correct to avoid penalizing
+ print("Failed to parse gold solution: ", sol)
+ continue
+
+ answer_parsed = parse(
+ content,
+ extraction_config=[
+ LatexExtractionConfig(
+ normalization_config=NormalizationConfig(
+ nits=False,
+ malformed_operators=False,
+ basic_latex=True,
+ equations=True,
+ boxed=True,
+ units=True,
+ ),
+ boxed_match_priority=0,
+ try_extract_without_anchor=False,
+ )
+ ],
+ extraction_mode="first_match",
+ )
+ correctness.append(verify(answer_parsed, gold_parsed))
+
+ # Calculate lengths
+ lengths = [len(content) for content in contents]
+ min_len = min(lengths)
+ max_len = max(lengths)
+
+ # If all responses have the same length, return zero rewards
+ if max_len == min_len:
+ return [0.0] * len(completions)
+
+ rewards = []
+ for length, is_correct in zip(lengths, correctness):
+ lambda_val = 0.5 - (length - min_len) / (max_len - min_len)
+
+ if is_correct:
+ reward = lambda_val
+ else:
+ reward = min(0, lambda_val)
+
+ rewards.append(float(reward))
+
+ return rewards
+
+
+def get_cosine_scaled_reward(
+ min_value_wrong: float = -1.0,
+ max_value_wrong: float = -0.5,
+ min_value_correct: float = 0.5,
+ max_value_correct: float = 1.0,
+ max_len: int = 1000,
+):
+ def cosine_scaled_reward(completions, solution, **kwargs):
+ """Reward function that scales based on completion length using a cosine schedule.
+
+ Shorter correct solutions are rewarded more than longer ones.
+ Longer incorrect solutions are penalized less than shorter ones.
+
+ Args:
+ completions: List of model completions
+ solution: List of ground truth solutions
+
+ This function is parameterized by the following arguments:
+ min_value_wrong: Minimum reward for wrong answers
+ max_value_wrong: Maximum reward for wrong answers
+ min_value_correct: Minimum reward for correct answers
+ max_value_correct: Maximum reward for correct answers
+ max_len: Maximum length for scaling
+ """
+ contents = [completion[0]["content"] for completion in completions]
+ rewards = []
+
+ for content, sol in zip(contents, solution):
+ gold_parsed = parse(sol, extraction_mode="first_match", extraction_config=[LatexExtractionConfig()])
+ if len(gold_parsed) == 0:
+ rewards.append(1.0) # Skip unparseable examples
+ print("Failed to parse gold solution: ", sol)
+ continue
+
+ answer_parsed = parse(
+ content,
+ extraction_config=[
+ LatexExtractionConfig(
+ normalization_config=NormalizationConfig(
+ nits=False,
+ malformed_operators=False,
+ basic_latex=True,
+ equations=True,
+ boxed=True,
+ units=True,
+ ),
+ boxed_match_priority=0,
+ try_extract_without_anchor=False,
+ )
+ ],
+ extraction_mode="first_match",
+ )
+
+ is_correct = verify(answer_parsed, gold_parsed)
+ gen_len = len(content)
+
+ # Apply cosine scaling based on length
+ progress = gen_len / max_len
+ cosine = math.cos(progress * math.pi)
+
+ if is_correct:
+ min_value = min_value_correct
+ max_value = max_value_correct
+ else:
+ # Swap min/max for incorrect answers
+ min_value = max_value_wrong
+ max_value = min_value_wrong
+
+ reward = min_value + 0.5 * (max_value - min_value) * (1.0 + cosine)
+ rewards.append(float(reward))
+
+ return rewards
+
+ return cosine_scaled_reward
+
+
+def get_repetition_penalty_reward(ngram_size: int, max_penalty: float):
+ """
+ Computes N-gram repetition penalty as described in Appendix C.2 of https://arxiv.org/abs/2502.03373.
+ Reference implementation from: https://github.com/eddycmu/demystify-long-cot/blob/release/openrlhf/openrlhf/reward/repetition.py
+
+ Args:
+ ngram_size: size of the n-grams
+ max_penalty: Maximum (negative) penalty for wrong answers
+ """
+ if max_penalty > 0:
+ raise ValueError(f"max_penalty {max_penalty} should not be positive")
+
+ def zipngram(text: str, ngram_size: int):
+ words = text.lower().split()
+ return zip(*[words[i:] for i in range(ngram_size)])
+
+ def repetition_penalty_reward(completions, **kwargs) -> float:
+ """
+ reward function the penalizes repetitions
+ ref implementation: https://github.com/eddycmu/demystify-long-cot/blob/release/openrlhf/openrlhf/reward/repetition.py
+
+ Args:
+ completions: List of model completions
+ """
+
+ contents = [completion[0]["content"] for completion in completions]
+ rewards = []
+ for completion in contents:
+ if completion == "":
+ rewards.append(0.0)
+ continue
+ if len(completion.split()) < ngram_size:
+ rewards.append(0.0)
+ continue
+
+ ngrams = set()
+ total = 0
+ for ng in zipngram(completion, ngram_size):
+ ngrams.add(ng)
+ total += 1
+
+ scaling = 1 - len(ngrams) / total
+ reward = scaling * max_penalty
+ rewards.append(reward)
+ return rewards
+
+ return repetition_penalty_reward
+
+
+def extract_code(completion: str) -> str:
+ pattern = re.compile(r"```python\n(.*?)```", re.DOTALL)
+ matches = pattern.findall(completion)
+ extracted_answer = matches[-1] if len(matches) >= 1 else ""
+ return extracted_answer
+
+
+def code_reward(completions, **kwargs) -> list[float]:
+ """Reward function that evaluates code snippets using the E2B code interpreter.
+
+ Assumes the dataset contains a `verification_info` column with test cases.
+ """
+ if not is_e2b_available():
+ raise ImportError(
+ "E2B is not available and required for this reward function. Please install E2B with "
+ "`pip install e2b-code-interpreter` and add an API key to a `.env` file."
+ )
+
+ # TODO: add support for other languages in E2B: https://e2b.dev/docs/code-interpreting/supported-languages
+ """Returns a reward function that evaluates code snippets in a sandbox."""
+ evaluation_script_template = """
+ import subprocess
+ import json
+
+ def evaluate_code(code, test_cases):
+ passed = 0
+ total = len(test_cases)
+ exec_timeout = 5
+
+ for case in test_cases:
+ process = subprocess.run(
+ ["python3", "-c", code],
+ input=case["input"],
+ text=True,
+ capture_output=True,
+ timeout=exec_timeout
+ )
+
+ if process.returncode != 0: # Error in execution
+ continue
+
+ output = process.stdout.strip()
+ if output.strip() == case["output"].strip():
+ passed += 1
+
+ success_rate = (passed / total)
+ return success_rate
+
+ code_snippet = {code}
+ test_cases = json.loads({test_cases})
+
+ evaluate_code(code_snippet, test_cases)
+ """
+ code_snippets = [extract_code(completion[-1]["content"]) for completion in completions]
+ verification_info = kwargs["verification_info"]
+ scripts = [
+ evaluation_script_template.format(code=json.dumps(code), test_cases=json.dumps(json.dumps(info["test_cases"])))
+ for code, info in zip(code_snippets, verification_info)
+ ]
+ try:
+ rewards = run_async_from_sync(scripts, verification_info["language"])
+
+ except Exception as e:
+ print(f"Error from E2B executor: {e}")
+ rewards = [0.0] * len(completions)
+
+ return rewards
+
+
+def get_trajectories_format_reward(min_steps: int = 3, partial_reward: bool = True):
+ """Reward function that checks if the reasoning process follows the ReAct (Reasoning and Acting) format.
+
+ Args:
+ min_steps: Minimum number of reasoning steps required for full reward.
+ partial_reward: If True, provide partial rewards for partially correct formats.
+
+ Returns:
+ A reward function that evaluates compliance with the ReAct format.
+ """
+ # Pattern looks for ReAct format sequences: Thought, Action, Observation
+ thought_pattern = r"Thought:[\s\S]+?"
+ action_pattern = r"Action:[\s\S]+?"
+ observation_pattern = r"Observation:[\s\S]+?"
+
+ # Full ReAct step pattern (all three components)
+ react_step_pattern = rf"({thought_pattern}{action_pattern}{observation_pattern})"
+
+ # Final answer pattern
+ final_answer_pattern = r"Answer:[\s\S]+"
+
+ def trajectories_format_reward(completions, **kwargs):
+ """Evaluates if completions follow the ReAct format with proper Thought/Action/Observation sequences.
+
+ Args:
+ completions: List of model completions
+
+ Returns:
+ List of rewards between 0.0 and 1.0 based on format compliance
+ """
+ contents = [completion[0]["content"] for completion in completions]
+ rewards = []
+
+ for content in contents:
+ # Count full ReAct steps (Thought/Action/Observation sequences)
+ react_steps = re.findall(react_step_pattern, content)
+ num_steps = len(react_steps)
+
+ # Check for final answer
+ has_final_answer = bool(re.search(final_answer_pattern, content))
+
+ if num_steps >= min_steps and has_final_answer:
+ # Full reward for meeting minimum steps and having final answer
+ rewards.append(1.0)
+ elif not partial_reward:
+ # No partial reward if disabled
+ rewards.append(0.0)
+ else:
+ # Partial rewards based on components present
+ reward = 0.0
+
+ # Reward for steps (up to 0.7)
+ step_reward = min(0.7, (num_steps / min_steps) * 0.7)
+ reward += step_reward
+
+ # Reward for final answer (0.3)
+ if has_final_answer:
+ reward += 0.3
+
+ rewards.append(reward)
+
+ return rewards
+
+ return trajectories_format_reward
+
+def get_code_format_reward(language: str = "python"):
+ """Format reward function specifically for code responses.
+
+ Args:
+ language: Programming language supported by E2B https://e2b.dev/docs/code-interpreting/supported-languages
+ """
+ pattern = rf"^\n.*?\n \n\n.*?```{language}.*?```.*?\n $"
+
+ def code_format_reward(completions, **kwargs):
+ completion_contents = [completion[0]["content"] for completion in completions]
+ matches = [re.match(pattern, content, re.DOTALL | re.MULTILINE) for content in completion_contents]
+ return [1.0 if match else 0.0 for match in matches]
+
+ return code_format_reward
+
+
+def run_async_from_sync(scripts: list[str], language: str) -> list[float]:
+ """Function wrapping the `run_async` function."""
+ # Create a new event loop and set it
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+
+ try:
+ # Run the async function and get the result
+ rewards = loop.run_until_complete(run_async(scripts, language))
+ finally:
+ loop.close()
+
+ return rewards
+
+
+async def run_async(scripts: list[str], language: str) -> list[float]:
+ # Create the sandbox by hand, currently there's no context manager for this version
+ sbx = await AsyncSandbox.create(timeout=30, request_timeout=3)
+
+ # Create a list of tasks for running scripts concurrently
+ tasks = [run_script(sbx, script) for script in scripts]
+
+ # Wait for all tasks to complete and gather their results as they finish
+ results = await asyncio.gather(*tasks)
+ rewards = list(results) # collect results
+
+ # Kill the sandbox after all the tasks are complete
+ await sbx.kill()
+
+ return rewards
+
+
+async def run_script(sbx, script: str, language: str) -> float:
+ execution = await sbx.run_code(script, language=language)
+ try:
+ return float(execution.text)
+ except (TypeError, ValueError):
+ return 0.0
diff --git a/openmanus_rl/sft.py b/openmanus_rl/sft.py
new file mode 100644
index 00000000..718aa78f
--- /dev/null
+++ b/openmanus_rl/sft.py
@@ -0,0 +1,177 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Simplified supervised fine-tuning script for decoder language models.
+
+Usage:
+# Fine-tune a model on a single GPU
+python -m openmanus_rl.sft \
+ --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+ --dataset_name HuggingFaceH4/Bespoke-Stratos-17k \
+ --learning_rate 2.0e-5 \
+ --num_train_epochs 1 \
+ --packing \
+ --max_seq_length 4096 \
+ --per_device_train_batch_size 2 \
+ --gradient_accumulation_steps 8 \
+ --gradient_checkpointing \
+ --bf16 \
+ --logging_steps 5 \
+ --output_dir data/sft-output
+"""
+
+import logging
+import os
+import sys
+
+import datasets
+import torch
+import transformers
+from datasets import load_dataset
+from transformers import set_seed
+from transformers.trainer_utils import get_last_checkpoint
+from trl import (
+ ModelConfig,
+ ScriptArguments,
+ SFTTrainer,
+ TrlParser,
+ get_kbit_device_map,
+ get_peft_config,
+ get_quantization_config,
+)
+
+from .configs import SFTConfig
+from .utils import get_tokenizer
+
+logger = logging.getLogger(__name__)
+
+
+def init_wandb_training(training_args):
+ """
+ Helper function for setting up Weights & Biases logging tools.
+ """
+ if training_args.wandb_entity is not None:
+ os.environ["WANDB_ENTITY"] = training_args.wandb_entity
+ if training_args.wandb_project is not None:
+ os.environ["WANDB_PROJECT"] = training_args.wandb_project
+
+
+def main(script_args, training_args, model_args):
+ # Set seed for reproducibility
+ set_seed(training_args.seed)
+
+ ###############
+ # Setup logging
+ ###############
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%Y-%m-%d %H:%M:%S",
+ handlers=[logging.StreamHandler(sys.stdout)],
+ )
+ log_level = training_args.get_process_log_level()
+ logger.setLevel(log_level)
+ datasets.utils.logging.set_verbosity(log_level)
+ transformers.utils.logging.set_verbosity(log_level)
+ transformers.utils.logging.enable_default_handler()
+ transformers.utils.logging.enable_explicit_format()
+
+ logger.info(f"Model parameters {model_args}")
+ logger.info(f"Script parameters {script_args}")
+ logger.info(f"Training parameters {training_args}")
+
+ # Check for last checkpoint
+ last_checkpoint = None
+ if os.path.isdir(training_args.output_dir):
+ last_checkpoint = get_last_checkpoint(training_args.output_dir)
+ if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+ logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
+
+ if "wandb" in training_args.report_to:
+ init_wandb_training(training_args)
+
+ ################
+ # Load datasets
+ ################
+ dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
+
+ ################
+ # Load tokenizer
+ ################
+ tokenizer = get_tokenizer(model_args, training_args)
+ tokenizer.pad_token = tokenizer.eos_token
+
+ ###################
+ # Model init kwargs
+ ###################
+ logger.info("*** Initializing model kwargs ***")
+ torch_dtype = (
+ model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
+ )
+ quantization_config = get_quantization_config(model_args)
+ model_kwargs = dict(
+ revision=model_args.model_revision,
+ trust_remote_code=model_args.trust_remote_code,
+ attn_implementation=model_args.attn_implementation,
+ torch_dtype=torch_dtype,
+ use_cache=False if training_args.gradient_checkpointing else True,
+ device_map=get_kbit_device_map() if quantization_config is not None else None,
+ quantization_config=quantization_config,
+ )
+ training_args.model_init_kwargs = model_kwargs
+
+ ############################
+ # Initialize the SFT Trainer
+ ############################
+ trainer = SFTTrainer(
+ model=model_args.model_name_or_path,
+ args=training_args,
+ train_dataset=dataset[script_args.dataset_train_split],
+ processing_class=tokenizer,
+ peft_config=get_peft_config(model_args),
+ )
+
+ ###############
+ # Training loop
+ ###############
+ logger.info("*** Train ***")
+ checkpoint = None
+ if training_args.resume_from_checkpoint is not None:
+ checkpoint = training_args.resume_from_checkpoint
+ elif last_checkpoint is not None:
+ checkpoint = last_checkpoint
+ train_result = trainer.train(resume_from_checkpoint=checkpoint)
+ metrics = train_result.metrics
+ metrics["train_samples"] = len(dataset[script_args.dataset_train_split])
+ trainer.log_metrics("train", metrics)
+ trainer.save_metrics("train", metrics)
+ trainer.save_state()
+
+ ##################################
+ # Save model
+ ##################################
+ logger.info("*** Save model ***")
+ trainer.save_model(training_args.output_dir)
+ logger.info(f"Model saved to {training_args.output_dir}")
+
+ # Restore k,v cache for fast inference
+ if trainer.accelerator.is_main_process:
+ trainer.model.config.use_cache = True
+ trainer.model.config.save_pretrained(training_args.output_dir)
+
+
+if __name__ == "__main__":
+ parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
+ script_args, training_args, model_args = parser.parse_args_and_config()
+ main(script_args, training_args, model_args)
diff --git a/openmanus_rl/utils.py b/openmanus_rl/utils.py
new file mode 100644
index 00000000..59077727
--- /dev/null
+++ b/openmanus_rl/utils.py
@@ -0,0 +1,38 @@
+import os
+
+from transformers import AutoTokenizer, PreTrainedTokenizer
+from trl import ModelConfig
+
+from .configs import GRPOConfig, SFTConfig
+
+DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
+
+
+def get_tokenizer(
+ model_args: ModelConfig, training_args: SFTConfig | GRPOConfig, auto_set_chat_template: bool = True
+) -> PreTrainedTokenizer:
+ """Get the tokenizer for the model."""
+ tokenizer = AutoTokenizer.from_pretrained(
+ model_args.model_name_or_path,
+ revision=model_args.model_revision,
+ trust_remote_code=model_args.trust_remote_code,
+ )
+
+ if training_args.chat_template is not None:
+ tokenizer.chat_template = training_args.chat_template
+ elif auto_set_chat_template and tokenizer.get_chat_template() is None:
+ tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE
+
+ return tokenizer
+
+
+
+
+def init_wandb_training(training_args):
+ """
+ Helper function for setting up Weights & Biases logging tools.
+ """
+ if training_args.wandb_entity is not None:
+ os.environ["WANDB_ENTITY"] = training_args.wandb_entity
+ if training_args.wandb_project is not None:
+ os.environ["WANDB_PROJECT"] = training_args.wandb_project
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 00000000..eba0e8b4
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+[[package]]
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+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
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+[metadata]
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..15c56343
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,84 @@
+[tool.poetry]
+name = "openmanus-rl"
+version = "0.0.1"
+description = "RL framework for OpenManus"
+authors = ["OpenManus-RL team"]
+license = "Apache 2.0 License"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.10, <3.12"
+accelerate = "1.4.0"
+bitsandbytes = ">=0.43.0"
+datasets = ">=3.2.0"
+deepspeed = "0.15.4"
+distilabel = { version = ">=1.5.2", extras = ["vllm", "ray", "openai"] }
+e2b-code-interpreter = ">=1.0.5"
+einops = ">=0.8.0"
+hf_transfer = "*"
+huggingface-hub = { version = ">=0.19.2,<1.0", extras = ["cli"] }
+langdetect = "*"
+latex2sympy2_extended = ">=1.0.6"
+liger_kernel = "0.5.3"
+lighteval = { git = "https://github.com/huggingface/lighteval.git", rev = "ed084813e0bd12d82a06d9f913291fdbee774905" }
+math-verify = "0.5.2"
+packaging = ">=23.0"
+parameterized = ">=0.9.0"
+peft = ">=0.14.0"
+python-dotenv = "*"
+safetensors = ">=0.3.3"
+sentencepiece = ">=0.1.99"
+torch = "2.5.1"
+transformers = "4.49.0"
+vllm = "0.7.2"
+wandb = ">=0.19.1"
+trl = { git = "https://github.com/huggingface/trl.git", rev = "69ad852e5654a77f1695eb4c608906fe0c7e8624" }
+
+[tool.poetry.group.dev.dependencies]
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+isort = ">=5.12.0"
+ruff = ">=0.9.0"
+
+[tool.poetry.group.test.dependencies]
+pytest = "*"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.mypy]
+ignore_missing_imports = true
+check_untyped_defs = true
+follow_imports = "normal"
+strict = true
+plugins = ["pydantic.mypy"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = "test_*.py"
+
+[tool.codespell]
+ignore-words-list = "dout, te, indicies, astroid"
+skip = ["data"]
+
+[tool.isort]
+profile = "black"
+use_parentheses = true
+skip_gitignore = true
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+line_length = 88
+
+[tool.black]
+line-length = 88
+target-version = ['py37', 'py38', 'py39', 'py310']
+
+[tool.ruff]
+line-length = 88
+fix = true
+target-version = "py310"
+
+[tool.ruff.format]
+quote-style = "single"
+indent-style = "space"
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..7de44c25
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,29 @@
+accelerate==1.4.0
+bitsandbytes>=0.43.0
+datasets>=3.2.0
+deepspeed==0.15.4
+distilabel[vllm,ray,openai]>=1.5.2
+e2b-code-interpreter>=1.0.5
+einops>=0.8.0
+flake8>=6.0.0
+hf_transfer>=0.1.4
+huggingface-hub[cli]>=0.19.2,<1.0
+isort>=5.12.0
+langdetect
+latex2sympy2_extended>=1.0.6
+liger_kernel==0.5.3
+git+https://github.com/huggingface/lighteval.git@ed084813e0bd12d82a06d9f913291fdbee774905
+math-verify==0.5.2
+packaging>=23.0
+parameterized>=0.9.0
+peft>=0.14.0
+pytest
+python-dotenv
+ruff>=0.9.0
+safetensors>=0.3.3
+sentencepiece>=0.1.99
+torch==2.5.1
+transformers==4.49.0
+git+https://github.com/huggingface/trl.git@69ad852e5654a77f1695eb4c608906fe0c7e8624
+vllm==0.7.2
+wandb>=0.19.1
diff --git a/scripts/.gitkeep b/scripts/.gitkeep
new file mode 100644
index 00000000..e69de29b
+ 
+ 
+ 
+