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We train CODESKILL with reinforcement learning, using a hybrid reward that combines dense rubric-based skill-quality feedback with sparse verifiable executi","title":"CODESKILL: Learning Self-Evolving Skills for Coding Agents","url":"https://arxiv.org/abs/2605.25430","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.25430v1 Announce Type: new \nAbstract: Coding agents produce rich trajectories while solving software-engineering tasks. To enable agent self-evolution, these trajectories can be distilled into reusable procedural skills that compactly encode experience to guide future behavior. However, existing skill construction and maintenance methods often rely on fixed prompts and heuristic update rules, leaving it unclear how knowledge should be selected, abstracted, and maintained to best serve downstream agents. We propose CODESKILL, an LLM-based framework that reformulates skill extraction and skill-bank maintenance as a learnable management policy. 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