LightRFT

Light, Efficient, Omni-modal & Reward-model Driven Reinforcement Fine-Tuning Framework

English | 简体中文

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📖 Introduction

LightRFT (Light Reinforcement Fine-Tuning) is an advanced reinforcement learning fine-tuning framework designed for Large Language Models (LLMs) and Vision-Language Models (VLMs). This framework provides efficient and scalable RLHF (Reinforcement Learning from Human Feedback) and RLVR training capabilities, supporting multiple state-of-the-art algorithms and distributed training strategies.

✨ Key Features

• 🚀 High-Performance Inference Engines

  • Integrated vLLM and SGLang for efficient sampling and inference
  • FP8 inference optimization for significantly reduced latency and memory usage
  • Flexible engine sleep/wake mechanisms for optimal resource utilization

• 🧠 Rich Algorithm Ecosystem

  • Policy Optimization: GRPO, GSPO, GMPO, Dr.GRPO
  • Advantage Estimation: REINFORCE++, CPGD
  • Reward Processing: Reward Norm/Clip
  • Sampling Strategy: FIRE Sampling, Token-Level Policy
  • Stability Enhancement: DAPO, select_high_entropy_tokens

• 🔧 Flexible Training Strategies

  • FSDP (Fully Sharded Data Parallel) v2 support
  • DeepSpeed ZeRO (Stage 1/2/3) support
  • Gradient checkpointing and mixed precision training (BF16/FP16)
  • Adam Offload and memory optimization techniques

• 🎯 Innovative Resource Collaboration

  • Colocate Anything: Co-locate reward models with training models to maximize GPU utilization
    • Support multiple reward models for parallel inference on the same device
    • Dynamic memory management with automatic training/inference phase switching
    • Reduced cross-device communication overhead for improved end-to-end training efficiency
  • Balance Anything 🚧 (Under Development): Intelligent load balancing system
    • Adaptive task scheduling and resource allocation
    • Automatic load balancing for multi-node training
    • Performance optimization for heterogeneous hardware environments

• 🌐 Comprehensive Multimodal Support

  • Native Vision-Language Model (VLM) Training
    • Support for mainstream VLMs like Qwen-VL
    • Parallel processing of multimodal image-text data
    • Efficient multimodal tokenization and batching
  • Multimodal Reward Modeling
    • Support for multiple visual reward models working in collaboration
    • Joint optimization of image understanding and text generation
  • Complete Vision-Language Alignment Training Pipeline
    • Optimized for multimodal RLVR/RLHF training
    • Built-in support for vision-language model fine-tuning

• 📊 Complete Experimental Toolkit

  • Weights & Biases (W&B) integration
  • Math capability benchmarking (GSM8K, Geo3K, etc.)
  • Trajectory saving and analysis tools
  • Automatic checkpoint management

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🎯 Supported Algorithms

For detailed algorithm descriptions, implementation details, and usage guide, see Algorithm Documentation.

Algorithm	Type	Key Improvement	Paper
GRPO	Policy Optimization	Group normalized advantage estimation	arXiv:2402.03300
GSPO	Policy Optimization	Group sequence policy optimization	arXiv:2507.18071
GMPO (WIP)	Policy Optimization	Geometric-mean policy optimization	arXiv:2507.20673
Dr.GRPO	Policy Optimization	Length bias mitigation	arXiv:2503.20783
DAPO	Policy Optimization	Decoupled clip and dynamic sampling policy optimization	arXiv:2503.14476
REINFORCE++	Advantage Estimation	Improved baseline estimation	arXiv:2501.03262
CPGD	Advantage Estimation	KL-based drift constraint	arXiv:2505.12504
FIRE Sampling	Sampling Strategy	Filtering and ranking strategies	arXiv:2410.21236

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🚀 Quick Start

Requirements

• Python >= 3.10
• CUDA >= 12.8
• PyTorch >= 2.5.1

Docker Images

TO BE DONE

Installation

Clone and install LightRFT:

# Clone the repository
git clone https://github.com/opendilab/LightRFT.git
cd LightRFT

# Install dependencies
pip install -r requirements.txt

# Install LightRFT
pip install -e .

📚 Usage Guide

Basic Example: GRPO Training

# Single node, 8 GPU training example
cd LightRFT

# Run GRPO training (GSM8K math reasoning task)
bash examples/gsm8k_geo3k/run_grpo_gsm8k_qwen2.5_0.5b.sh

# Or run Geo3K geometry problem training (VLM multimodal)
bash examples/gsm8k_geo3k/run_grpo_geo3k_qwen2.5_vl_7b.sh

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🏗️ Project Structure

LightRFT/
├── lightrft/                      # Core library
│   ├── strategy/                  # Training & inference strategies
│   │   ├── fsdp/                  # FSDP implementation
│   │   ├── deepspeed/             # DeepSpeed implementation
│   │   ├── vllm_utils/            # vLLM utilities
│   │   ├── sglang_utils/          # SGLang utilities
│   │   └── utils/                 # Strategy utilities
│   ├── models/                    # Model definitions
│   │   ├── actor_al.py            # Audio-language model actor
│   │   ├── actor_language.py      # Language model actor
│   │   ├── actor_vl.py            # Vision-language model actor
│   │   ├── grm_vl.py              # Generative reward model (Vision-Language)
│   │   ├── srm_al.py              # Scalar reward model (Audio-Language)
│   │   ├── srm_vl.py              # Scalar reward model (Vision-Language)
│   │   ├── loss.py                # Loss functions
│   │   ├── monkey_patch/          # Model adaptation patches for distributed training
│   │   ├── tests/                 # Model tests
│   │   └── utils.py               # Model utilities
│   ├── trainer/                   # Trainer implementations
│   │   ├── ppo_trainer.py         # LLM PPO trainer
│   │   ├── ppo_trainer_vl.py      # VLM PPO trainer
│   │   ├── spmd_ppo_trainer.py    # SPMD PPO trainer Extension (**Core**)
│   │   ├── grm_trainer_vl.py      # Generative reward model trainer (Vision-Language)
│   │   ├── srm_trainer_al.py      # Scalar reward model trainer (Audio-Language)
│   │   ├── srm_trainer_vl.py      # Scalar reward model trainer (Vision-Language)
│   │   ├── fast_exp_maker.py      # Fast experience generator (**Core**)
│   │   ├── experience_maker.py    # Base experience generator
│   │   ├── experience_maker_vl.py # Base experience generator for VLM
│   │   ├── replay_buffer.py       # Replay buffer
│   │   ├── replay_buffer_vl.py    # VLM replay buffer
│   │   ├── replay_buffer_utils.py # Replay buffer utilities
│   │   ├── kl_controller.py       # KL divergence controller
│   │   └── utils.py               # Trainer utilities
│   ├── datasets/                  # Dataset processing
│   │   ├── audio_alpaca.py        # Audio Alpaca dataset
│   │   ├── grm_dataset.py         # Generative reward model dataset
│   │   ├── hpdv3.py               # HPDv3 reward model dataset
│   │   ├── image_reward_db.py     # Image reward database
│   │   ├── imagegen_cot_reward.py # Image generation CoT generative reward
│   │   ├── omnirewardbench.py     # OmniRewardBench dataset
│   │   ├── process_reward_dataset.py # Reward dataset processing
│   │   ├── prompts_dataset.py     # LLM Prompts dataset
│   │   ├── prompts_dataset_vl.py  # Vision-language prompts dataset
│   │   ├── rapidata.py            # Rapidata reward modeldataset
│   │   ├── sft_dataset.py         # SFT dataset
│   │   ├── sft_dataset_vl.py      # VLM SFT dataset
│   │   ├── srm_dataset.py         # Scalar reward model base dataset
│   │   └── utils.py               # Dataset utilities
│   └── utils/                     # Utility functions
│       ├── ckpt_scripts/          # Checkpoint processing scripts
│       ├── cli_args.py            # CLI argument parsing
│       ├── distributed_sampler.py # Distributed sampler
│       ├── logging_utils.py       # Logging utilities
│       ├── processor.py           # Data processor for HF model
│       ├── remote_rm_utils.py     # Remote reward model utilities
│       ├── timer.py               # Timer utilities
│       ├── trajectory_saver.py    # Trajectory saver
│       └── utils.py               # General utilities
│
├── examples/                      # Usage examples
│   ├── gsm8k_geo3k/               # GSM8K/Geo3K math reasoning training examples
│   ├── grm_training/              # Generative reward model training examples
│   ├── srm_training/              # Scalar reward model training examples
│   ├── chat/                      # Model dialogue examples
│
├── docs/                          # 📚 Sphinx documentation
│   ├── Makefile                   # Documentation build Makefile
│   ├── make.bat                   # Documentation build batch file
│   └── source/                    # Documentation source
│       ├── _static/               # Static files (CSS, etc.)
│       ├── api_doc/               # API documentation
│       ├── best_practice/         # Best practices & resources
│       ├── installation/          # Installation guides
│       └── quick_start/           # Quick start & user guides
│
├── assets/                        # Assets
│   └── logo.png                   # Project logo
│
├── CHANGELOG.md                   # Changelog
├── LICENSE                        # License file
├── Makefile                       # Project Makefile
├── README.md                      # Project documentation (English)
├── README_zh.md                   # Project documentation (Chinese)
├── requirements.txt               # Python dependencies
├── requirements-dev.txt           # Development dependencies
├── requirements-doc.txt           # Documentation dependencies
└── setup.py                       # Package setup script

🔑 Key Directory Descriptions

• lightrft/: LightRFT core library, providing training strategies, model definitions, and trainer implementations
• examples/: Complete training examples and scripts
  • gsm8k_geo3k/: GSM8K and Geo3K math reasoning training examples
  • grm_training/: Generative reward model training examples
  • srm_training/: Scalar reward model training examples
  • chat/: Model dialogue examples
• docs/: Sphinx documentation with complete user guides and API documentation

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⚙️ Key Configuration Parameters

Batch Size Configuration

TBS=128                           # Training batch size
RBS=128                            # Rollout batch size
micro_train_batch_size=1          # Micro batch size per GPU
micro_rollout_batch_size=2        # Rollout micro batch size

Algorithm Parameters

--advantage_estimator group_norm  # Advantage estimator: group_norm, reinforce, cpgd
--n_samples_per_prompt 8          # Number of samples per prompt
--max_epochs 1                    # Training epochs per episode
--num_episodes 3                  # Total training episodes
--kl_estimator k3                 # KL estimator type
--init_kl_coef 0.001              # KL penalty coefficient

Distributed Training

--fsdp                            # Enable FSDP
--zero_stage 3                    # DeepSpeed ZeRO Stage
--gradient_checkpointing          # Gradient checkpointing
--adam_offload                    # Adam optimizer offload
--bf16                            # BF16 mixed precision

Inference Engine

--rm_use_engine                   # Use inference engine (vLLM/SGLang)
--engine_mem_util 0.4             # Engine memory utilization
--engine_tp_size 1                # Engine tensor parallelism degree
--enable_engine_sleep             # Enable engine sleep mechanism

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🔧 Troubleshooting

See training scripts for detailed parameter validation logic.

1. OOM (Out of Memory)

Solutions:

• Reduce micro_train_batch_size and micro_rollout_batch_size
• Enable --gradient_checkpointing
• Lower --engine_mem_util
• Use ZeRO Stage 3

2. Training Instability

Solutions:

• Enable Reward Normalization: --normalize_reward
• Lower learning rate
• Use --advantage_estimator group_norm
• Try DAPO algorithm

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📖 Documentation

📚 Complete Documentation Guide

Quick Start:

• Installation Guide - Docker images, installation methods, and troubleshooting
• Supported Algorithms - Comprehensive algorithm guide with implementation details
• Configuration Reference - Complete parameter documentation

Best Practices:

• Training Strategy Usage - FSDP, DeepSpeed, and inference engine configuration
• FAQ - Frequently asked questions and solutions
• Troubleshooting Guide - Common issues and debugging
• Contributing Guide - How to contribute to LightRFT

Build Documentation Locally

Install documentation dependencies:

pip install -r requirements-doc.txt

Generate HTML documentation:

make docs
# Open docs/build/index.html to view documentation

Live documentation preview:

make docs-live
# Visit http://localhost:8000

🤝 Contributing

We welcome and appreciate contributions from the community! To ensure a smooth workflow, please follow these steps:

1. Fork the Repository: Click the "Fork" button at the top right to copy this project to your GitHub account.
2. Create a Feature Branch: Create a new branch for your changes, preferably based on main. Ensure documentation branches are named with the doc pattern to enable auto-deployment of the docs site.

   git checkout -b feature/your-feature-name

3. Commit Your Changes: Please follow the Conventional Commits specification.
   • Format example: feature(user): short description of the change
   • Common types: feature (new feature), fix (bug fix), polish (polish or optimize), docs (documentation), style (formatting), refactor (code restructuring).

   git commit -m 'feature(user): add an amazing feature'

4. Push to the Branch: Push your changes to your forked repository.

   git push origin feature/your-feature-name

5. Open a Pull Request: Go to the original repository and create a Pull Request targeting the main (or specific development) branch. Please provide a detailed description of your changes.

Code Standards

# Install development dependencies
pip install -r requirements-dev.txt

# Code formatting (YAPF)
make format

# Code linting (Flake8)
make fcheck

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📚 Citation

If you use this codebase in your research or applications, please cite it as follows:

@misc{lightrft,
  title={LightRFT},
  author={Niu, Yazhe and Pu, Yuan and Shi, Dongxing and Lu, Yudong and Xiong, Yingtong and Ge, Ruijun and Sun, Jiaxuan and Wan, Zunian and Zhang, Shaoang and others},
  publisher={GitHub},
  howpublished={\url{https://github.com/opendilab/LightRFT}},
  year={2025},
}

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

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🙏 Acknowledgments

LightRFT is developed based on OpenRLHF. We extend our sincere gratitude to the OpenRLHF team for their excellent work. Some files and implementations in this project are adapted and reused from OpenRLHF.

Collaboration

This project is developed in collaboration with colleagues from the System Platform Center and Safe and Trustworthy AI Center at Shanghai AI Laboratory. We sincerely thank them for their contributions and support.

Open Source Dependencies

This project builds upon the following outstanding open-source projects (including but not limited):

• OpenRLHF, verl - Core RL framework foundation (parts of key components adapted and reused)
• vLLM - High-performance inference engine
• SGLang - Structured generation language runtime
• DeepSpeed - Distributed training optimization
• PyTorch FSDP - Fully Sharded Data Parallel

Thanks to all contributors and supporters!

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📮 Contact

For questions or suggestions, please contact us via:

• Issues: GitHub Issues
• Email: opendilab@pjlab.org.cn

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⭐ If this project helps you, please give us a star!

Made with ❤️ by LightRFT Team