Skills and Challenges Co-Evolve: Learnability-Aware Autocurricula for Agent Skill Evolution

Large language model agents use reusable skills to solve multi-step tasks that require reasoning, tool use, and executable procedures. Existing methods typically optimize skills against largely fixed objectives or generate evaluation tasks separately from skill learning. This separation can favor verifier-specific solutions and produce challenges that provide little learning signal or exceed the skill population's capabilities. We introduce SCOPE, Skill and Challenge Optimization through co-Population Evolution, a learnability-aware autocurriculum that jointly evolves candidate skills and executable challenges near the changing capability boundary of the skill population. SCOPE evaluates each challenge by how well it discriminates among skills, provides actionable signals for attainable improvement, and reveals previously uncovered failures. A solvability gate filters invalid or unsupported challenges through executable verification, reference procedures, or independently verified model consensus. The surviving challenges guide skill mutation and selection. Structured mutations vary tools, schemas, information availability, dependencies, and feedback signals. Hall-of-fame replay maintains pressure from previously informative challenges and reduces regressions on acquired capabilities. We compare SCOPE with fixed-task optimization, random domain perturbation, and adversarial challenge generation without learnability control under hidden tool versions, unseen difficulty ranges, and composed perturbations. Controlled analyses examine evolutionary dynamics, post-evolution transfer, failure coverage, and the individual contributions of learnability, solvability validation, replay, and mutation diversity.