新的蒸馏方法提高了小型语言模型的推理能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一种新的方法，可以将大型语言模型的推理链蒸馏到小型模型中，即使在无法完全访问教师模型的情况下也是如此。这种递归离线蒸馏技术结合了基于熵的采样来选择性地选择推理链，以及答案混合来用真实数据进行补充。对三个小型开放模型的实验表明，这种方法显著优于朴素蒸馏，在可比的数据预算下实现了更好的性能，并且学习曲线更快。 AI

影响该方法可以通过改善大型模型到小型模型的知识转移，从而实现更高效的专门化小型语言模型的训练。

排序理由该集群包含一篇详细介绍新型LLM蒸馏方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Andrey Goncharov, Daniil Vyazhev, Simon Epanov, Alexey Zaytsev · 2026-10-16 04:00

递归式离线蒸馏通过熵增采样和答案混合实现

arXiv:2610.20914v1 Announce Type: new Abstract: Distillation of reasoning chains from Large Language Models (LLMs) into their smaller siblings is a standard training approach. Frequently, researchers do not have full access to a teacher model and its internal representations, whi…

本文如何被排名

报道来源 [1]

递归式离线蒸馏通过熵增采样和答案混合实现

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