English(EN) Learning Process Rewards via Reasoning State Propagation

新的RSP方法通过结果监督增强AI推理训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种名为推理状态传播（RSP）的新方法，以改进AI推理过程奖励模型（PRM）的训练。RSP通过有效利用结果监督来指导中间推理状态的学习，解决了过程标注成本高昂的挑战。通过对推理轨迹中有效性状态之间的转换进行建模，RSP将中间步骤与最终结果联系起来，从而在束搜索和强化学习等任务中提高了性能。在评估中，与Qwen2.5-Math-PRM基线相比，RSP在束搜索方面平均提高了5.6%，在强化学习方面平均提高了2.1%。 AI

影响通过提高中间步骤的训练效率和有效性，增强了AI的推理能力。

排序理由该集群包含一篇详细介绍AI训练新方法的论文。[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) · Kai Gan, Zi-Hao Zhou, Bo Ye, Jian Zhao, Min-Ling Zhang, Tong Wei · 2026-10-01 04:00

通过推理状态传播实现学习过程奖励

arXiv:2609.39220v1 Announce Type: new Abstract: Process reward models (PRMs) have demonstrated notable effectiveness in test-time scaling and reinforcement learning by providing fine-grained signals for evaluating intermediate reasoning states, but their training relies heavily o…

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通过推理状态传播实现学习过程奖励

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