AI

Sherpa: Teaching LLMs to Teach Adaptively

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Briefings

Sherpa is a reinforcement learning framework that trains LLM teachers to adapt instruction for diverse student archetypes. The system improves student performance by 20.5 percentage points on MathTutorBench and achieves a 79.6% success rate in human preference comparisons over base models. This work demonstrates that LLMs can be conditioned on distinct learning preferences to become better aligned with human teachers.

Key points

• Sherpa uses multi-turn reinforcement learning to train LLM teachers that maximize individual student learning outcomes.
• Teacher models trained with Sherpa improve student performance by an average of 20.5 percentage points on MathTutorBench.

Context

Existing LLM training methods rely on demonstrations or predefined criteria, which do not account for individual student learning variations. Sherpa addresses this by conditioning teachers on distinct student archetypes to adapt instruction effectively.

Limitations

Evidence is limited to simulated student archetypes and MathTutorBench metrics; human studies show preference alignment but do not confirm real-world student outcomes.

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