COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization

• URL: http://arxiv.org/abs/2609.11682v1
• Date: Thu, 10 Sep 2026 15:12:24 GMT
• Title: COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization
• Abstract summary: Large language model (LLM) agents can benefit from reusable skills distilled from prior task experience.<n>Existing skill optimization methods often rely on costly execution-based evaluation and substantial task data.<n>We introduce textbfCOBRA-Skills, an efficient framework that formulates skill optimization as budgeted sequential optimization over a dynamically evolving candidate space.
• Score: 55.60171725587438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) agents can benefit from reusable skills distilled from prior task experience, yet existing skill optimization methods often rely on costly execution-based evaluation and substantial task data. We introduce \textbf{COBRA-Skills}, an efficient framework that formulates skill optimization as budgeted sequential optimization over a dynamically evolving candidate space. COBRA-Skills couples contextual-bandit-guided prioritization with evidence-grounded skill evolution, selectively allocating evaluations to promising or informative candidates while continually refining the skill population from execution feedback. Across six heterogeneous agent benchmarks and three target models, COBRA-Skills consistently achieves the strongest average performance among compared methods, while reducing optimization cost by 55--58\% relative to SkillOpt and using only 50 unique optimization examples per benchmark. Further analyses show that COBRA-Skills remains robust to changes in the agent harness and performs effectively when the target model itself is used for skill generation and refinement.

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