AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback
The paper presents Adaptive Group Policy Optimization (AGPO), a new method for improving reinforcement learning in large language models. AGPO utilizes group-level statistics to enhance training efficiency by controlling update magnitude and exploration. The results indicate that models trained with AGPO outperform traditional methods on various benchmarks.
- ▪AGPO is a critic-free refinement of GRPO that enhances training stability.
- ▪It employs adaptive clipping and bidirectional adaptive temperature sampling for better performance.
- ▪Models trained with AGPO achieved significant improvements on math and STEM benchmarks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20722 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.20722 (cs) [Submitted on 20 May 2026] Title:AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback Authors:Miaobo Hu, Shuhao Hu, Bokun Wang, Ruohan Wang, Xin Wang, Xiaobo Guo, Daren Zha, Jun Xiao View a PDF of the paper titled AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback, by Miaobo Hu and 6 other authors View PDF Abstract:Reinforcement learning improves LLM reasoning, but PPO/GRPO typically use fixed clipping and decoding temperature, which makes training brittle and tuning-heavy. We propose Adaptive Group Policy Optimization (AGPO), a critic-free refinement of GRPO that uses group-level statistics to control both update magnitude and exploration.
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