Open Source RL Libraries for LLMs

Reinforcement learning (RL) is increasingly crucial for developing large language models (LLMs), extending beyond traditional reinforcement learning from human feedback (RLHF) to include verifiable rewards, especially as high-quality pre-training data becomes scarce. Recent advancements highlight this approach's success, exemplified by OpenAI's reasoning models and DeepSeek R1 models. The field is rapidly evolving with open-source RL libraries that reflect diverse design philosophies and optimization strategies. These libraries, including TRL, Verl, OpenRLHF, RAGEN, AReaL, Verifiers, ROLL, NeMo-RL, and SkyRL, offer various features tailored for different RL use cases, such as RLHF, reasoning, and agentic RL, and are assessed based on their flexibility, scalability, and design components like the generator and trainer. The analysis conducted aims to guide researchers and practitioners in selecting suitable tools by providing insights into each library's strengths, weaknesses, and use cases. The choice of RL library depends on specific user requirements, whether focused on performance, flexibility, or the ability to handle multi-turn interactions within environments.

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