Hugging Face Daily PapersPingchen Lu, Xiangyi Wang, Xiang Li1 min readpaperadvanced
COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization
Summary
COBRA‑Skills uses a contextual‑bandit loop to selectively evaluate and evolve LLM agent skills, achieving better performance with roughly half the evaluation cost of prior methods. The framework works with limited examples and remains robust across different agent setups and even when the target model creates its own skills.
- COBRA‑Skills frames skill optimization as a budgeted sequential problem, using a contextual bandit to prioritize which candidate skills to evaluate next.
- The method cuts evaluation cost by roughly 55‑58% compared to prior SkillOpt while achieving higher average performance across six benchmarks.
- It requires only about 50 unique optimization examples per benchmark, making it data‑efficient for LLM‑agent development.
- The skill population is continuously refined from execution feedback, keeping the candidate set dynamic and evidence‑grounded.
LLM‑agent developers who need to optimize reusable skills efficiently will benefit from the cost‑effective, data‑light approach described.
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