Regularizing skill updates prevents overfitting in agent learning
SkillEvoReg: Regularizing Agent Skill Evolution Against Overfitting
Artificial Intelligence
Summary
Sometimes when AI agents learn new skills by updating themselves repeatedly, they can become too specialized or forget previous abilities. The authors call this problem skill-evolution overfitting. They created a method called SkillEvoReg that helps prevent this by randomly ignoring some updates, controlling complexity, and carefully checking for mistakes. Their method works with different skill-learning systems, keeps skills from growing unnecessarily, and improves learning in later stages without losing performance.
What this means in practice
- •For ai development teams: Maintain robustness of continually learning AI agents by preventing skill update overfitting to keep performance stable over time.
- •For robotics engineers: Improve skill adaptation in robots by controlling unnecessary complexity growth during incremental learning.
Authors
Guanyu Nie, Fangzhou Zhu, Shixiong Kai, Xiongwei Han, Tao Zhong, Mingxuan Yuan
Abstract
Language-model agents increasingly improve by converting execution experience into reusable external skills. Yet repeated skill updates form a learning process of their own: locally useful edits can accumulate into redundant or task-specific instructions, while new updates can disrupt behavior that previously worked. We study this problem as skill-evolution overfitting and introduce SkillEvoReg, a general regularization framework for skill evolution inspired by anti-overfitting techniques in neural-network training. SkillEvoReg combines training-time skill dropout, which perturbs update generation, and complexity-aware local regularization, which controls unnecessary structural growth, with causal counterexample validation (CCV), which provides targeted behavioral validation of candidate-specific regressions. We instantiate the framework across heterogeneous skill-evolution systems while retaining each system's native skill evolver and task evaluator. Across SkillOpt, SkillEvolBench, and ContinualSkillBench, SkillEvoReg consistently controls skill-state growth while preserving competitive downstream capability, improves several transfer and later-stage evolution outcomes, and identifies update-level regressions that structural metrics alone cannot reveal. These results suggest that explicit regularization is a useful complement to increasingly capable skill updaters.