Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills
Agent Skills improve LLM agents by making task knowledge reusable across sessions.
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Key points
- Learning transferable Skills requires evidence about a procedure and its conditions of use, which a successful task attempt can leave untested.
- We introduce Socratic Skill Learning (SSL), which interleaves expert teaching with Student practice.
- To test reuse beyond the learning task, we construct SkillTransferBench, with 35 task families and 408 executable cases, pairing public variation specifications with held-out evaluation in fresh solver sessions.
- Across three model pairs on shared families, interleaved guidance outperforms expert authoring and post-hoc teaching.
Sources (1)
- [1]Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent SkillsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 07:23 AM
Agent Skills improve LLM agents by making task knowledge reusable across sessions.
Learning transferable Skills requires evidence about a procedure and its conditions of use, which a successful task attempt can leave untested.
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