Learner2Skill：将学习者行为转化为可复用模拟技能的 LLM 学习者模拟方法
From Learner Behavior to Reusable Skills for Effective and Efficient Learner Simulation
AI 导读
研究者提出 Learner2Skill，把从历史交互中学到的学习者模拟能力外化为持久、可复用的 Simulation Skill，捕捉学习者的学习状态与重复应答模式，并随新交互演化。
正文 · 原文
Abstract:Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine-grained learning behaviors, existing approaches often need to repeatedly process a growing interaction history to reconstruct the learner. This introduces additional context and inference costs and makes the acquired learner-specific simulation capability difficult to reuse across different LLMs. We therefore propose Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill. The Skill captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration without reconstructing the learner from scratch. Experiments show that Learner2Skill more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.
| Comments: | 16 pages |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.37157 [cs.AI] |
| (or arXiv:2609.37157v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.37157 arXiv-issued DOI via DataCite |
Submission history
From: Zijian Chen [view email]
[v1]
Tue, 29 Sep 2026 09:47:57 UTC (9,727 KB)
[v2]
Fri, 2 Oct 2026 15:05:09 UTC (9,725 KB)
[v3]
Thu, 5 Nov 2026 02:30:18 UTC (8,947 KB)
来源：arXiv cs.AI · arxiv.org