Sherpa: a multi-turn RL framework that trains LLMs to teach adaptively
An arXiv preprint introduces Sherpa, a multi-turn reinforcement learning framework that instantiates multiple student archetypes with distinct learning preferences and trains a teacher model to adapt its instruction by directly maximizing those students' learning outcomes. The authors report that Sherpa-trained teachers improve instructed students' performance by an average of 20.5 percentage points across all archetypes, and raise the overall pedagogy score on MathTutorBench from 52.5% to 79.2%. In human studies, the trained teacher was preferred over the base model in 79.6% of pairwise comparisons; the 32-page paper says code and model are available.
Why it matters: It separates being able to solve a problem from being able to teach it, and optimizes instruction against measurable student outcomes rather than predefined pedagogical criteria.
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
View a PDF of the paper titled Sherpa: Teaching LLMs to Teach Adaptively, by Weixian Xu and 4 other authors
View PDF HTML (experimental)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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View a PDF of the paper titled Sherpa: Teaching LLMs to Teach Adaptively, by Weixian Xu and 4 other authors
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