{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dapo-an-open-source-llm-reinforcement","title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","arxiv_id":"2503.14476","date":"2025-03-18","proceeding":null,"authors":["Qiying Yu","Zheng Zhang","Ruofei Zhu","Yufeng Yuan","Xiaochen Zuo","Yu Yue","Weinan Dai","Tiantian Fan","Gaohong Liu","Lingjun Liu","Xin Liu","Haibin Lin","Zhiqi Lin","Bole Ma","Guangming Sheng","Yuxuan Tong","Chi Zhang","Mofan Zhang","Wang Zhang","Hang Zhu","Jinhua Zhu","Jiaze Chen","Jiangjie Chen","Chengyi Wang","Hongli Yu","Yuxuan Song","Xiangpeng Wei","Hao Zhou","Jingjing Liu","Wei-Ying Ma","Ya-Qin Zhang","Lin Yan","Mu Qiao","Yonghui Wu","Mingxuan Wang"],"abstract":"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 $\\textbf{D}$ecoupled Clip and $\\textbf{D}$ynamic s$\\textbf{A}$mpling $\\textbf{P}$olicy $\\textbf{O}$ptimization ($\\textbf{DAPO}$) 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.","url_abs":"https://arxiv.org/abs/2503.14476v2","url_pdf":"https://arxiv.org/pdf/2503.14476v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dapo-an-open-source-llm-reinforcement","repo_url":"https://github.com/tiger-ai-lab/vl-rethinker","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2503.14476","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}