When Non-Adversarial Noise Hurts Learning: Benchmarking Skill Self-Evolution in Agents
Large language model agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rules and affect later tasks. Existing evaluations largely examine clean learning or perturbations encountered after skills are frozen, leaving robustness during self-evol...
Tao Yu, Zhong-Tian Luo, Yu-Chen Long et al.
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