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SkillLift: Learning Dense Rubrics from Sparse Oracles for Efficient Skill Evolution

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

LLM-based agents increasingly rely on persistent skills, i.e., reusable procedural prompts, to adapt without weight updates. Existing skill self-evolution methods directly revise skill text based on execution feedback, but each oracle evaluation requires a full agent rollout, creating a supervision bottleneck that confines search to failure-patching updates. Our key insight is that ranking is a smoother supervision target than absolute outcome regression: identifying which skill is better requires fewer oracle evaluations than predicting exact scores. Building on this insight, we propose Skill

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