[2510.15863] PolySkill: Learning Generalizable Skills Through Polymorphic Abstraction

Computer Science > Computation and Language arXiv:2510.15863 (cs) [Submitted on 17 Oct 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:PolySkill: Learning Generalizable Skills Through Polymorphic Abstraction Authors:Simon Yu, Gang Li, Weiyan Shi, Peng Qi View a PDF of the paper titled PolySkill: Learning Generalizable Skills Through Polymorphic Abstraction, by Simon Yu and 3 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are moving beyond static uses and are now powering agents that learn continually during their interaction with external environments. For example, agents can learn reusable skills while navigating web pages or toggling new tools. However, existing methods for skill learning often create skills that are over-specialized to a single website and fail to generalize. We introduce PolySkill, a new framework that enables agents to learn generalizable and compositional skills. The core idea, inspired by polymorphism in software engineering, is to decouple a skill's abstract goal (what it accomplishes) and its concrete implementation (how it is executed). Experiments show that our method (1) improves skill reuse by 1.7x on seen websites and (2) boosts success rates by up to 9.4% on Mind2Web and 13.9% on unseen websites, while reducing steps by over 20%. (3) In self-exploration settings without specified tasks, our framework improves the quality of proposed tasks and enables agents to learn generalizable skills that wor...