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COBRA-Skills: Contextual Bandit-Guided Evolution fo... | AI Research

Key Takeaways

  • Large language model (LLM) agents often struggle to perform consistently across recurring tasks, and existing methods for creating "skills"—reusable procedur...
  • 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.
  • 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.
  • Large language model (LLM) agents often struggle to perform consistently across recurring tasks, and existing methods for creating "skills"—reusable procedural knowledge—are often inefficient.
Paper AbstractExpand

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.

Large language model (LLM) agents often struggle to perform consistently across recurring tasks, and existing methods for creating "skills"—reusable procedural knowledge—are often inefficient. They typically require expensive, repetitive execution of tasks to determine if a skill is useful, wasting significant computational resources. COBRA-Skills addresses this by treating skill optimization as a budgeted, sequential process. It uses a smart prioritization system to decide which skills to test, ensuring that the most promising or informative candidates receive the most attention while minimizing unnecessary evaluations.

How COBRA-Skills Works

The framework functions as a closed loop between two main components: contextual-bandit-guided prioritization and evidence-grounded evolution. The same large language models question is explored in Efficient Test-Time Adaptation through Human-AI Interaction, which adds a research perspective.
Instead of testing every possible skill, the system uses a "contextual bandit" approach. It represents each skill as a semantic embedding and uses a lightweight neural network to predict its potential performance. It then adds an uncertainty bonus to this prediction, which encourages the system to test both high-performing skills and those that are less understood. Periodically, the system prunes low-performing or redundant skills and replaces them with new ones generated through three evolutionary methods:

  • Regeneration: Creating entirely new skills from scratch based on initial task data to explore new strategies.

  • Rollout Mutation: Refining an existing skill by analyzing the specific successes and failures from its recent execution.

  • Crossover: Combining strategies from high-performing skills while using low-performing ones as negative examples to avoid past mistakes.

Efficiency and Performance

COBRA-Skills is designed to be highly cost-effective. By intelligently selecting which candidates to evaluate, it significantly reduces the number of times the target agent must run through a task. In tests across six diverse benchmarks—ranging from mathematical reasoning to embodied decision-making—the framework consistently outperformed existing methods. The ai agents story also surfaces in Stanford Researchers Develop TRACE to Fix..., adding another angle.
Compared to previous approaches like SkillOpt, COBRA-Skills reduced the total optimization cost by 55% to 58%. It achieved these results using only 50 unique optimization examples per benchmark, demonstrating that it can learn high-quality skills without needing massive amounts of task data.

Robustness and Flexibility

The research highlights that the benefits of COBRA-Skills are not limited to a specific setup. The framework proved robust when tested across different agent harnesses, meaning it can adapt to various execution environments without losing effectiveness. Furthermore, the system remains successful even when the target model itself is used to generate and refine its own skills—a "self-teaching" setting that eliminates the need for an external, more powerful teaching model. This makes COBRA-Skills a versatile tool for improving agent performance in resource-constrained scenarios. The ai agents story also surfaces in Claude autonomously improved models across 10..., adding another angle. as detailed in the full paper on Arxiv

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