Reinforcement Learning for Self-Improving Agent with Skill Library

ArXi:2512.17102v2 Announce Type: replace Large Language Model (LLM)-based agents have nstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills. However, current skill library approaches rely primarily on LLM prompting, making consistent skill library implementation challenging.