Sherpa：用多轮强化学习训练LLM自适应教学

Hugging Face · Papers10-05 20:00Weixian Xu, Yanzhe Zhang, Zora Zhiruo Wang, Changyu Chen, Diyi Yang

AI 总结

arXiv 预印本提出 Sherpa，一个多轮强化学习框架：它用不同学习偏好设定多个“学生原型”，让教师模型通过直接最大化学生的学习效果来调整教学策略。论文称，经 Sherpa 训练的教师模型使各类原型学生的表现平均提升 20.5 个百分点，在 MathTutorBench 上把整体教学法得分从 52.5% 提升到 79.2%。人类研究显示，该教师模型在 79.6% 的成对比较中优于基线模型，论文共 32 页，代码与模型已公开。

为什么重要：它把“会解题”与“会教学”区分开来，尝试用可量化的学生学习结果而非预设教学标准来训练 AI 教师，为 AI 辅导走向真实学习者提供了一条可验证的路径。

SherpaMathTutorBench

来源原文Hugging Face · Papers · 约 3 分钟读完

Computer Science > Artificial Intelligence

arXiv:2610.08778 (cs)

[Submitted on 6 Oct 2026]

Title:Sherpa: Teaching LLMs to Teach Adaptively

Authors:Weixian Xu, Yanzhe Zhang, Zora Zhiruo Wang, Changyu Chen, Diyi Yang

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Abstract:Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach it. Existing approaches to training LLMs as teachers rely on demonstrations, preference data, or predefined pedagogical criteria that specify what good teaching looks like. However, these signals are often not grounded in individual student learning outcomes, where effective teaching strategies can vary substantially across learners. To address this, we introduce Sherpa, a multi-turn reinforcement learning framework that instantiates multiple student archetypes with LLMs conditioned on distinct learning preferences and trains a teacher model to adapt its instruction by directly maximizing their learning outcomes. Teacher LLMs trained with Sherpa improve instructed students' performance across all archetypes by an average of 20.5 percentage points. Under MathTutorBench's evaluation, Sherpa raises the overall pedagogy score from 52.5% to 79.2%, indicating better teaching responses. Our human studies show that the trained teacher is preferred over the base model in 79.6% of pairwise comparisons. Together, Sherpa trains LLM teachers to adapt to diverse simulated students and become better aligned with human teachers, paving the road towards AI tutors teaching real students.

Comments:	32 pages, 6 figures. Code and model are available at this https URL
Subjects:	Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as:	arXiv:2610.08778 [cs.AI]
	(or arXiv:2610.08778v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.08778 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Weixian Xu [view email]
[v1] Tue, 6 Oct 2026 17:58:18 UTC (585 KB)

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