arxiv:2609.11682
Published on Sep 10
· Submitted by
Jerry smith on Sep 14
· Chinese University of Hong Kong, Shenzhen
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Abstract
COBRA-Skills improves LLM agent skill optimization by using contextual-bandit prioritization and evidence-based evolution to cut evaluation costs while maintaining high performance.
Generated by thinkingmachines/Inkling-Small
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 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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Paper author Paper submitter 9 days ago
How can we optimize Agent Skills without repeatedly spending large amounts of computation on weak candidates and costly LLM-based refinement?
COBRA-Skills treats skill optimization as a sequential budget-allocation problem:
Which skill is most worth evaluating next?
Instead of exhaustively trying and refining candidate skills, COBRA-Skills maintains a population of skills and uses:
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a neural predictor to estimate skill utility,
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LinearUCB to balance exploration and exploitation,
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target-agent execution feedback to update the bandit,
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scheduled evolution operators (regeneration, rollout mutation, and crossover) to evolve the skill population.
Results
We evaluate COBRA-Skills on 6 diverse agent benchmarks × 3 target models, covering search QA, spreadsheets, document understanding, mathematical reasoning, social reasoning, and embodied tasks.
COBRA-Skills achieves:
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🏆 the best average performance among the compared methods,
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💰 ~55–58% lower optimization cost than SkillOpt,
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📊 only 50 optimization examples per benchmark,
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🔧 consistent effectiveness under Codex and Claude Code harnesses,
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🤖 strong performance even when the target model itself generates and refines skills.
An interesting finding is that the cost reduction does not mainly come from reducing target-agent executions. A large part comes from avoiding repeated LLM-based trajectory analysis and skill rewriting.
📄 Paper: https://arxiv.org/abs/2609.11682 💻 Code: https://github.com/Jerry-LuP/COBRA-Skills
Feedback and discussions are very welcome!
8 days ago
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