SkillLift: Learning Dense Rubrics from Sparse Oracles for Efficient Skill Evolution
Read the original on arXiv AI →SkillLift introduces a method for efficiently evolving reusable procedural prompts (skills) in large language model agents by learning a dense rubric that aligns with sparse oracle evaluations. Instead of directly revising skill text based on costly full agent rollouts, the approach decouples skill search from oracle cost through a bilevel optimization framework: an inner loop uses a frozen rubric as a cheap surrogate to guide skill updates, while an outer loop periodically realigns the rubric using a small number of oracle rollouts via rank correlation. Experiments on complex agent task benchmarks demonstrate that SkillLift outperforms existing auto-skill methods while reducing token cost by 40–70% compared to frontier-evolving approaches.
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