Group Sequence Policy Optimization
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.
68831fe6846d2e78b7548ac8
Chujie Zheng
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2025-07-25T13:36:28.634Z
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Chujie Zheng
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chujiezheng
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Shixuan Liu
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2025-07-29T07:10:41.029Z
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Shixuan Liu
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liusx
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Mingze Li
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Xiong-Hui Chen
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2025-07-25T13:36:26.841Z
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Xiong-Hui Chen
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xionghuichen
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Bowen Yu
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Chang Gao
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Kai Dang
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Yuqiong Liu
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Rui Men
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An Yang
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2025-07-28T08:46:14.463Z
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An Yang
user
yangapku
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Jingren Zhou
68831fe6846d2e78b7548ad3
Junyang Lin
Group Sequence Policy Optimization
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.
Group Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm that improves training efficiency and performance of large language models by using sequence-level importance ratios and operations.
Group Sequence Policy Optimization GSPO reinforcement learning large language models sequence likelihood sequence-level clipping rewarding optimization GRPO Mixture-of-Experts MoE RL Qwen3