English(EN) When KL Regularization Misfires in Group Policy Optimization

新研究识别出群体策略优化中的KL正则化失败

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

一篇题为《当KL正则化在群体策略优化中失灵时》的新研究论文探讨了KL正则化在群体策略优化中的问题。该论文确定了KL正则化可能对性能产生负面影响的七种潜在故障模式，例如奖励裁剪后的残差更新或梯度抵消。为了解决这些问题，研究人员提出了零和校准策略优化（ZCPO），这是一种使用条件KL校准组内奖励系数并将其整合到基础代理中的方法。 AI

影响这项研究可能为大型语言模型和其他强化学习系统的更稳定有效的训练方法带来突破。

排序理由该集群包含一篇详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Fei Ding · 2026-10-09 04:00

当KL正则化在群体策略优化中失灵时

arXiv:2610.12161v1 Announce Type: cross Abstract: Why does removing reference-policy KL regularization sometimes improve group policy optimization? This motivates studying how reference-policy information should enter group-relative updates. We analyze seven potential failure mod…

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当KL正则化在群体策略优化中失灵时

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