Installation | Usage | Examples | Citation

---

Updates

• 31/10/2025: We advocate for re-evaluating precision choices in RL training (Precision RL) — demonstrating that FP16 offers superior performance and stability compared to the de facto BF16.
• 02/10/2025: We add LoRA-RL support and validate its performance as comparable to full fine-tuning RL (super excited to be highlighted by John Schulman).
• 21/03/2025: We incorporate Dr. GRPO, which fixes the optimization bias in GRPO.
• 26/01/2025: We support reinforcement learning with verifiable rewards (RLVR) for math reasoning.
• 20/10/2024: We open source Oat, an online LLM alignment framework developed during a research project on online LLM exploration (sample-efficient alignment).

Introduction

Oat 🌾 is a simple yet efficient framework for running online LLM alignment algorithms. Its key features include:

• High Efficiency: Oat implements a distributed Actor-Learner-Oracle architecture, with each component being optimized using state-of-the-art tools:
  • Actor: Utilizes vLLM for accelerated online response sampling.
  • Learner: Leverages DeepSpeed ZeRO strategies to enhance memory efficiency.
  • Oracle: Model-based oracle by Mosec as a remote service, supporting dynamic batching, data parallelism and pipeline parallelism.
• Simplified Workflow: Oat simplifies the experimental pipeline of LLM alignment. With an Oracle served online, we can flexibly query it for preference data labeling as well as anytime model evaluation. All you need is to launch experiments and monitor real-time learning curves (e.g., win rate) on wandb (see reproduced results) — no need for manual training, checkpointing and loading for evaluation.
• Oracle Simulation: Oat provides a diverse set of oracles to simulate preference/reward/verification feedback.
  • Verifiable rewards supported using rule-based functions.
  • Lightweight reward models run within the actor's process, enabling quick testing on as few as two GPUs.
  • Larger and more capable reward models can be served remotely, harnessing additional compute and memory resources.
  • LLM-as-a-judge is supported via querying OpenAI API for model-based pairwise ranking.
• Ease of Use: Oat's modular structure allows researchers to easily inherit and modify existing classes, enabling rapid prototyping and experimentation with new algorithms.
• Cutting-Edge Algorithms: Oat implements state-of-the-art online algorithms, fostering innovation and fair benchmarking.
  • PPO/Dr.GRPO (online RL) for math reasoning.
  • Online DPO/SimPO/IPO for online preference learning.
  • Online exploration (active alignment) algorithms, including SEA, APL and XPO.

Installation

In a python environment with supported versions (we recommend 3.10), you could install oat via PyPI:

pip install vllm==0.8.4 && pip install -U oat-llm

Or you could also install in "editable" mode for local development:

git clone git@github.com:sail-sg/oat.git
cd oat
pip install vllm==0.8.4 && pip install -e .

Usage

• R1-Zero-like training using Dr. GRPO for math reasoning: a single-file self-contained implementation with training script.

• Multi-turn SFT: an example training script.

• Online preference learning with active exploration: a detailed guide.

Adopters

Research projects that are built (or integrated) with Oat 🌾:

• Understanding R1-Zero-Like Training: A Critical Perspective
• VeriFree: Reinforcing General Reasoning without Verifiers
• SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning
• GEM: A Gym for Agentic LLMs

Citation

If you find this codebase useful for your research, please consider citing:

• LLM online alignment framework:

  @misc{liu2024oat,
    title={OAT: A research-friendly framework for LLM online alignment},
    author={Liu, Zichen and Chen, Changyu and Wan, Xinyi and Du, Chao and Lee, Wee Sun and Lin, Min},
    year={2024}
    howpublished={\url{https://github.com/sail-sg/oat}},
  }
  

• Online exploration method:

  @article{liu2024sea,
    title={Sample-Efficient Alignment for LLMs},
    author={Liu, Zichen and Chen, Changyu and Du, Chao and Lee, Wee Sun and Lin, Min},
    journal={arXiv preprint arXiv:2411.01493},
    year={2024}
  }
  

License

oat is distributed under the terms of the Apache2 license.

Acknowledgement

We thank the following awesome projects that have contributed to the development of oat:

• vLLM
• DeepSpeed
• Mosec
• launchpad
• OpenRLHF

Disclaimer

This is not an official Sea Limited or Garena Online Private Limited product.