# Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills

arXiv: https://arxiv.org/abs/2610.17860 · HTML: https://mlsanity.com/paper/2610.17860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 26 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Placed in

- LLM agents and tool use / [Agent skill learning and evolution](https://mlsanity.com/agents/agent-skill-learning-and-evolution/)

## Builds on (most cited first, 22 of 22)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://mlsanity.com/paper/2601.11868/) (2026)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills](https://mlsanity.com/paper/2603.25158/) (2026)
- [SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation](https://mlsanity.com/paper/2406.14991/) (2024)
- [FullStack Bench: Evaluating LLMs as Full Stack Coders](https://mlsanity.com/paper/2412.00535/) (2024)
- [SkillLearnBench: Benchmarking Continual Learning Methods for Agent Skill Generation on Real-World Tasks](https://mlsanity.com/paper/2604.20087/) (2026)
- [SkillGen: Verified Inference-Time Agent Skill Synthesis](https://mlsanity.com/paper/2605.10999/) (2026)
- [SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision](https://mlsanity.com/paper/2606.01139/) (2026)
- [CodeTracer: Towards Traceable Agent States](https://mlsanity.com/paper/2604.11641/) (2026)
- [Failure as a Process: An Anatomy of CLI Coding Agent Trajectories](https://mlsanity.com/paper/2607.09510/) (2026)
- [AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios](https://mlsanity.com/paper/2601.20613/) (2026)
- [Harnessing LLM Agents with Skill Programs](https://mlsanity.com/paper/2605.17734/) (2026)
- [SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History](https://mlsanity.com/paper/2606.08671/) (2026)
- [ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?](https://mlsanity.com/paper/2608.03874/) (2026)
- [SKILL-KD: Contrastive Skill Distillation for LLM Agents](https://mlsanity.com/paper/2607.28048/) (2026)
- [AgentBrew: Lifelong Knowledge Brewing from Strong Teachers to Weak LLM Agents](https://mlsanity.com/paper/2607.16851/) (2026)
- [EconSkills: Studying Skill Transfer and Retrieval for Web Agents on Live Economic Data](https://mlsanity.com/paper/2609.19523/) (2026)

## Abstract (arXiv)

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. We introduce Socratic Skill Learning (SSL), which interleaves expert teaching with Student practice. An Educator guides investigation while the Student remains the sole environment operator and Skill author, able to question, test, and adapt advice. 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. On the main 22-family, 155-case cohort, Fixed-Window SSL reaches 0.801 utility, versus 0.723 without a Skill, 0.761 for adapted Trace2Skill (trajectory distillation), and 0.751 for adapted SKILL-KD (contrastive distillation). Across three model pairs on shared families, interleaved guidance outperforms expert authoring and post-hoc teaching. Process analysis links guidance to Student writing and package revision; a case study shows the Student testing and refining a proposed procedure for later reuse. As demand for new lessons falls, the cost of fixed-window review grows. Issue-Guided SSL tracks unresolved questions across Skill revisions to select and focus review, reaching 0.805 utility with 61.7% fewer Educator tokens and 16.9% fewer total acquisition tokens than Fixed-Window SSL on the main cohort.