Group Sequence Policy Optimization

Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu +7 more
7/24/2025
cs.LGcs.AIcs.CL

No official code found. Best implementation: chenyuntc/simple-gspo (8 stars, last commit 2025-08-20).

Abstract

This paper introduces Group Sequence Policy Optimization (GSPO), our stable, efficient, and performant reinforcement learning algorithm for training large language models. Unlike previous algorithms that adopt token-level importance ratios, GSPO defines the importance ratio based on sequence likelihood and performs sequence-level clipping, rewarding, and optimization. We demonstrate that GSPO achieves superior training efficiency and performance compared to the GRPO algorithm, notably stabilizes Mixture-of-Experts (MoE) RL training, and has the potential for simplifying the design of RL infrastructure. These merits of GSPO have contributed to the remarkable improvements in the latest Qwen3 models.

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Code Implementations(1)

A minimal implementation of GSPO (Group Sequence Policy Optimization)

81PythonAug 20, 20251 years ago

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Cite this paper

@article{zheng2025group,
  title  = {Group Sequence Policy Optimization},
  author = {Chujie Zheng and Shixuan Liu and Mingze Li and Xiong-Hui Chen and Bowen Yu and Chang Gao and Kai Dang and Yuqiong Liu and Rui Men and An Yang and Jingren Zhou and Junyang Lin},
  year   = {2025},
  eprint = {2507.18071},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.18071v2}
}

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