English(EN) When All Rollouts Fail: Policy-Compatible Joint Decoding for Hard-Problem Reasoning

新PICO方法增强LLM在复杂问题上的推理能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员推出了一种名为策略兼容强化学习（PICO）的新方法，旨在提高大型语言模型在复杂问题上的推理能力。PICO通过执行分布约束的联合解码，解决了现有可验证奖励强化学习（RLVR）的局限性。该方法使用专家数据指导令牌级解码，同时保持学生模型的相对令牌概率并约束采样分布。实验表明，PICO在各种推理数据集和代理基准测试中，始终优于标准的RLVR和其他非策略基线。 AI

影响通过提供比现有强化学习方法更强的学习信号，增强LLM在复杂任务上的推理能力。

排序理由该集群包含一篇详细介绍改进LLM推理新方法的论文。[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) · Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang, Lin Yang · 2026-10-28 04:00

当所有部署都失败时：策略兼容的联合解码用于困难问题推理

arXiv:2610.36849v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) derives its learning signal from binary rewards via on-policy exploration and becomes a powerful paradigm for improving the reasoning of large language models. Yet it faces a l…

本文如何被排名

报道来源 [1]

当所有部署都失败时：策略兼容的联合解码用于困难问题推理

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