English(EN) From Solver Trajectories to Teaching Trajectories: Cognition-Aligned Reasoning Distillation

新框架对齐AI推理轨迹以改进知识蒸馏

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

研究人员引入了认知对齐的推理蒸馏（CARD）框架，旨在改进从大型推理模型（LRMs）到小型模型的知识蒸馏。传统方法通常使用原始教师轨迹，这些轨迹针对求解而非教学进行优化，导致对于认知能力不同的小型模型效率低下。CARD将现有的求解轨迹转化为更符合学生模型学习能力的教学轨迹。这通过分布对齐奖励和逻辑连贯性奖励实现，有助于匹配学生的预测分布并减少误导性推理步骤，最终优于原始轨迹蒸馏。 AI

影响该框架通过改进知识从更大、更强大的模型到小型AI模型的转移方式，可能带来更高效的小型AI模型训练。

排序理由该集群包含一篇研究论文，详细介绍了用于AI模型蒸馏的新框架。[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) · Yashuo Luo, Qianren Mao, Ziqi Qin, Junnan Liu, Haitao Li, Qili Zhang, Yuan Gao, Zhijun Chen, Xu Wang · 2026-10-28 04:00

从求解轨迹到教学轨迹：认知对齐的推理蒸馏

arXiv:2610.36445v1 Announce Type: new Abstract: Knowledge distillation from large reasoning models (LRMs) often uses raw teacher trajectories as supervision. However, this practice creates a mismatch between the goal of distillation and the nature of teacher trajectories. Advance…

本文如何被排名

报道来源 [1]

从求解轨迹到教学轨迹：认知对齐的推理蒸馏

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