Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning

ArXi:2605.06130v1 Announce Type: new A persistent skill library allows language model agents to reuse successful strategies across tasks. Maintaining such a library requires three coupled capabilities. The agent selects a relevant skill, utilizes it during execution, and distills new skills from experience. Existing methods optimize these capabilities in isolation or with separate reward sources, resulting in partial and conflicting evolution.