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skill-optimization

topic2 events
papersTODAY 04:00 UTC

MOSCOPT Method Optimizes Multiple LLM Agent Skills Together

A new arXiv paper introduces MOSCOPT, an approach that jointly optimizes collections of prompts and skills for LLM agents rather than refining a single text template. The authors argue that existing prompt and skill optimization methods miss beneficial interactions between multiple skills used by an agent. The work is a research preprint and has not yet been peer reviewed.

agents/prompt-optimization/llm/research
papersSEP 12 04:00 UTC

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

A new arXiv preprint proposes COBRA-Skills, a method that applies contextual bandit guidance to evolve reusable skills for large language model agents. The approach aims to cut the reliance on expensive execution-based evaluation and large task datasets that limit existing skill optimization techniques. It targets agents that reuse skills distilled from earlier task experience.

agents/llm-agents/reinforcement-learning/arxiv