SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills
The paper introduces SkillEvolBench, a benchmark designed to evaluate the transition from episodic experience to procedural skills in large language model agents. It consists of 180 tasks across various environments, focusing on the ability of agents to form reusable skills from their experiences. The findings indicate that while agents can adapt locally, they often struggle to develop robust skills, with raw-trajectory reuse frequently outperforming distilled skills.
- ▪SkillEvolBench evaluates the evolution from episodic experience to procedural skills in AI agents.
- ▪The benchmark includes 180 tasks organized into role-conditioned task families.
- ▪Current agents often adapt locally but rarely form durable reusable skills.
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Computer Science > Artificial Intelligence arXiv:2605.24117 (cs) [Submitted on 22 May 2026] Title:SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills Authors:Yingtie Lei, Zhongwei Wan, Jiankun Zhang, Samiul Alam, Zixuan Zhong, Peizhou Huang, Xin Wang, Jingxuan Zhang, Donghao Zhou, Yunta Hsieh, Zhihao Dou, Hui Shen, Yan Xu, Dimitrios Dimitriadis, Tuo Zhang, Mi Zhang View a PDF of the paper titled SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills, by Yingtie Lei and 15 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) agents accumulate rich episodic trajectories while solving real-world tasks, but it remains unclear whether such experience can be distilled into reusable procedural…
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