Group Sequence Policy Optimization

• URL: http://arxiv.org/abs/2507.18071v2
• Date: Mon, 28 Jul 2025 11:11:33 GMT
• Title: Group Sequence Policy Optimization
• Authors: Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, Jingren Zhou, Junyang Lin,
• Abstract summary: Group Sequence Policy Optimization (GSPO) is a stable, efficient, and performant reinforcement learning algorithm.<n>GSPO defines the importance ratio based on sequence likelihood and performs sequence-level clipping, rewarding, and optimization.
• Score: 55.40088895148603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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