OpenMLRL/CoMLRL

Democratizing Reinforcement Learning for LLMs

Dr. MAS is an end-to-end RL training framework for multi-agent LLM systems, supporting the co-training of multiple (heterogeneous) LLMs.

SkyRL: A Modular Full-stack RL Library for LLMs

An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)

Scalable toolkit for efficient model reinforcement

Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning

An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models

The complete post-training stack for LLMs, plus RL for any problem. SFT, DPO and multi-turn agentic RL, with evolutionary hyperparameter optimization built in.