Lifetime Learning of Skills: Evolving the Skill-Learning Algorithm Itself

Large language model agents can acquire reusable procedural knowledge as skills. Existing skill-learning methods typically use fixed refinement lifecycles that prescribe which execution evidence to inspect, how to revise skills, how to allocate the evaluation budget, and when to deploy or revert updates. These methods apply the same learning strategy across heterogeneous failures, domains, and stages of skill maturity. We introduce Lifetime Skill-Learning (LSL), a framework that evolves the algorithm for constructing, evaluating, and maintaining skills while leaving the foundation model and host agent unchanged. LSL represents the skill-learning lifecycle as an executable meta-program that controls trajectory selection, failure diagnosis, update-operator selection, candidate generation, budget allocation, testing, compression, deployment, and rollback. An inner loop executes each meta-program to construct and validate domain skills. An outer population-based loop evolves the meta-programs using cross-task regret, anytime performance, budget efficiency, transfer, lifecycle complexity, and safety-constraint violations. This separation allows the learning strategy to adapt and records an executable, replayable history of how each deployed skill was obtained. We evaluate LSL using a cross-domain meta-learning protocol with structurally diverse domains for meta-training and unseen domains for transfer evaluation. We compare LSL with fixed refinement loops, expert-designed policies, single-prompt meta-skills, and population-based alternatives. Controlled component ablations further test whether lifecycle evolution provides benefits beyond additional sampling and repeated skill rewriting.