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

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 deploying these skills to accomplish real-world tasks. The r

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ethics.ai (10 August 2026), “SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models,” evidence record 19509, https://ethics.ai/record/19509 (originally published by HuggingFace Daily Papers).

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