[Paper Reading]: SkillOpt: Executive Strategy for Self-Evolving Agent Skills

Why we picked it

This week, we will walk through and discuss the paper: SkillOpt: Executive Strategy for Self-Evolving Agent Skills [https://arxiv.org/pdf/2605.23904] **Abstract of the Paper:** Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns scored rollouts into bounded add/delete/replace edits on a single skill document, and an edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, rejected-edit buffer, and epoch-wise slow/meta update make skill traini