SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

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arXiv:2608.10538v2 Announce Type: replace Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution. However, because strong closed-source models entail high inference costs, current popular agent harnesses, such as Codex and OpenClaw, remain prohibitively expensive when deployin