Abstract

This paper introduces Group Sequence Policy Optimization (GSPO), our stable,efficient, and performant reinforcement learning algorithm for training largelanguage models. Unlike previous algorithms that adopt token-level importanceratios, GSPO defines the importance ratio based on sequence likelihood andperforms sequence-level clipping, rewarding, and optimization. We demonstratethat GSPO achieves superior training efficiency and performance compared to theGRPO algorithm, notably stabilizes Mixture-of-Experts (MoE) RL training, andhas the potential for simplifying the design of RL infrastructure. These meritsof GSPO have contributed to the remarkable improvements in the latest Qwen3models.