README
rLLM
Train your AI agents with RL. Any framework. Minimal code changes.
rLLM is an open-source framework for training AI agents with reinforcement learning. Swap in a tracked client, define a reward function, and let RL handle the rest — no matter what agent framework you use.
Core Features
- Works with any agent framework — LangGraph, SmolAgent, Strands, OpenAI Agents SDK, Google ADK, or plain
openai.OpenAI. Just swap the client. 🔌 - Near-zero code changes — Add
@rllm.rolloutto wrap your agent code, and rLLM traces every LLM call automatically. 🪄 - CLI-first workflow — Eval and train from the command line with 50+ built-in benchmarks.
rllm eval gsm8kjust works. ⚡ - Battle-tested results — rLLM-trained agents beat models 50x their size (4B → outperforms 235B on finance, 1.5B → surpasses O1-Preview on math). 📈
- Multiple RL algorithms — GRPO, REINFORCE, RLOO, rejection sampling, and more. 🧠
- Two training backends —
verlfor distributed multi-GPU training,tinkerfor single-machine / CPU setups. Same API either way. 🔧
Read more on our documentation site.