新方法在无教师完整思维链的情况下提炼大语言模型推理能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种名为Stepwise Querying的新方法，可以将大型语言模型的推理能力提炼到更小的模型中，即使教师模型仅提供最终答案和简要解决方案。该技术通过对中间推导和有效性检查进行独立查询来引发特定步骤的解释，然后将这些解释组装起来进行监督微调。该方法在多个基准测试中表现出卓越的性能，在大多数比较中优于基线，并引入了一个名为Stepwise Explanation Information Gain (SEIG) 的诊断工具来分析推理轨迹。 AI

影响能够更有效地将复杂的推理技能转移到更小、更易于访问的AI模型中。

排序理由该集群包含一篇详细介绍提炼大语言模型推理能力新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Dicong Yu, Wenhua Wang, Tian Wang · 2026-11-02 05:00

Stepwise-Querying: Distilling Reasoning without Access to Teacher Chain-of-Thought

arXiv:2610.41769v1 Announce Type: new Abstract: Reasoning distillation is widely used to transfer the reasoning capabilities of large language models to smaller students through teacher-generated supervision. In the black-box setting, however, teachers may expose only final answe…

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Stepwise-Querying: Distilling Reasoning without Access to Teacher Chain-of-Thought

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