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Sherpa: Teaching LLMs to Teach Adaptively

Paper recorded by Signals 4 on 2026-10-06 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.AI · 人工智能 · first seen 2026-10-07

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 learnin

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#404 most recent of 960 cs.AI papers we have recorded · ↑ newer: DepthWorld: 3D World Model for Robot Manipulation · ↓ older: Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap,
Cite this page: Sherpa: Teaching LLMs to Teach Adaptively: the #404 most recent of 960 cs.AI papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/sherpa-teaching-llms-to-teach-adaptively.html
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