DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Authors: name: Qiying Yu, name: Zheng Zhang, name: Ruofei Zhu, name: Yufeng Yuan, name: Xiaochen Zuo, name: Yu Yue, name: Tiantian Fan, name: Gaohong Liu, name: Lingjun Liu, name: Xin Liu, name: Haibin Lin, name: Zhiqi Lin, name: Bole Ma, name: Guangming Sheng, name: Yuxuan Tong, name: Chi Zhang, name: Mofan Zhang, name: Wang Zhang, name: Hang Zhu, name: Jinhua Zhu, name: Jiaze Chen, name: Jiangjie Chen, name: Chengyi Wang, name: Hongli Yu, name: Weinan Dai, name: Yuxuan Song, name: Xiangpeng Wei, name: Hao Zhou, name: Jingjing Liu, name: Wei-Ying Ma, name: Ya-Qin Zhang, name: Lin Yan, name: Mu Qiao, name: Yonghui Wu, name: Mingxuan Wang

Inference scaling empowers LLMs with unprecedented reasoning ability, with
reinforcement learning as the core technique to elicit complex reasoning.
However, key technical details of state-of-the-art reasoning LLMs are concealed
(such as in OpenAI o1 blog and DeepSeek R1 technical report), thus the
community still struggles to reproduce their RL training results. We propose
the Decoupled Clip and Dynamic sAmpling
Policy Optimization (DAPO) algorithm, and
fully open-source a state-of-the-art large-scale RL system that achieves 50
points on AIME 2024 using Qwen2.5-32B base model. Unlike previous works that
withhold training details, we introduce four key techniques of our algorithm
that make large-scale LLM RL a success. In addition, we open-source our
training code, which is built on the verl framework, along with a carefully
curated and processed dataset. These components of our open-source system
enhance reproducibility and support future research in large-scale LLM RL.