新的RL方法W2SPO通过短辅助分支改进LLM推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-21 04:00

研究人员开发了一种新的离策略强化学习方法W2SPO，旨在提高大型语言模型的推理能力。该技术通过在模型轨迹中注入短的、8个token的辅助片段来解决标准方法中奖励对比度有限的问题。然后，策略更新将集中在这些片段上，利用最终可验证的奖励。W2SPO在4B规模模型的数学推理基准测试中表现出优越的性能，优于现有的后训练基线，并实现了显著的训练加速。 AI

影响该方法可以增强大型语言模型的推理能力，尤其是在数学问题解决等复杂任务中。

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]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Dayu Wang, Jiaye Yang, Weikang Li, Jiahui Liang, Liwei Qian, Xin Pei, Jizhou Huang · 2026-07-21 04:00

仅需8个Token：通过辅助分支实现弱到强离轨强化学习

arXiv:2607.16205v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has emerged as a standard approach for enhancing reasoning in large language models, which typically optimizes the policy by contrasting multiple self generated rollouts. However, we id…

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报道来源 [1]

仅需8个Token：通过辅助分支实现弱到强离轨强化学习

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