DAPO: An Open-Source LLM Reinforcement Learning System at Scale

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.

ByteCheckpoint: A Unified Checkpointing System for LLM Development

The development of real-world Large Language Models (LLMs) necessitates checkpointing of training states in persistent storage to mitigate potential software and hardware failures, as well as to facilitate checkpoint transferring within the training pipeline and across various tasks. Due to the immense size of LLMs, saving and loading checkpoints often incur intolerable minute-level stalls, significantly diminishing training efficiency. Besides, when transferring checkpoints across tasks, checkpoint resharding, defined as loading checkpoints into parallel configurations differing from those used for saving, is often required according to the characteristics and resource quota of specific tasks. Previous checkpointing systems [16,3,33,6] assume consistent parallel configurations, failing to address the complexities of checkpoint transformation during resharding. Furthermore, in the industry platform, developers create checkpoints from different training frameworks[23,36,21,11], each with its own unique storage and I/O logic. This diversity complicates the implementation of unified checkpoint management and optimization. To address these challenges, we introduce ByteCheckpoint, a PyTorch-native multi-framework LLM checkpointing system that supports automatic online checkpoint resharding. ByteCheckpoint employs a data/metadata disaggregated storage architecture, decoupling checkpoint storage from the adopted parallelism strategies and training frameworks. We design an efficient asynchronous tensor merging technique to settle the irregular tensor sharding problem and propose several I/O performance optimizations to significantly enhance the efficiency of checkpoint saving and loading. Experimental results demonstrate ByteCheckpoint's substantial advantages in reducing checkpoint saving (by up to 529.22X) and loading (by up to 3.51X) costs, compared to baseline methods.