DAPO: An Open-Source RL System from ByteDance Seed and Tsinghua Air

We release a fully open-sourced system for large-scale LLM RL, including algorithm, code infrastructure, and dataset. The system achieves state-of-the-art large-scale LLM RL performance. We propose the D ecoupled Clip and D ynamic s A mpling P olicy O ptimization ( DAPO ) algorithm. Through open-sourcing, we provide the broader research community and society with practical access to scalable reinforcement learning, enabling all to benefit from these advancements. Our system is based on the awesome verl framework. Thanks for their great work!

🤗 If you have any questions about our paper, issues are welcomed and we could discuss there. Thank you!

AIME 2024 Performance 🚀 DAPO achieves 50 points on AIME 2024 based on the Qwen2.5-32B base model, outperforming the previous SoTA DeepSeek-R1-Zero-Qwen-32B with 50% training steps.