English(EN) ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy

新的ACPO框架增强了大型语言模型的强化学习能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-07 04:00

研究人员推出了一种名为自适应信用策略优化（ACPO）的新框架，旨在改进大型语言模型强化学习中的信用分配。ACPO通过不对称地调整策略更新来解决稀疏奖励的挑战，重点关注成功试验中的不确定决策和失败试验中的过度自信的token。该方法旨在在保持策略梯度方向的同时，提高在AIME 2025和HumanEvalPro等基准测试上的性能，优于DAPO、GTPO和SAPO等现有方法。 AI

影响新的ACPO框架可能导致更高效的大型语言模型训练，从而提高它们在复杂推理和编码任务上的性能。

排序理由该集群包含一篇详细介绍人工智能强化学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zijun Xie, Yuyang You, Yongzhi Li, Enlei Gong, Zeyu Chen, Quan Chen, Yanhua Cheng, Peng Jiang, Yadong Mu · 2026-07-07 04:00

ACPO：通过细粒度代理熵实现自适应信用策略优化

arXiv:2607.03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make token-level credit assignment difficult. Existing scalable RL methods typically as…

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ACPO：通过细粒度代理熵实现自适应信用策略优化

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