Skills-Coach：一种通过无训练GRPO实现的自我进化技能优化器
Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO
AI总结：
Skills-Coach通过无训练GRPO框架提升LLM代理技能自我进化能力，包含四个核心模块，通过Skill-X基准测试展示其在多种技能类别中的显著性能提升。
摘要：
我们介绍了Skills-Coach，一种新的自动化框架，旨在显著增强基于大型语言模型（LLM）的代理中的技能自我进化能力。针对当前技能生态系统的碎片化问题，Skills-Coach探索技能能力的边界，从而促进智能应用所需的全面能力覆盖。该框架包含四个核心模块：多样任务生成模块系统地生成各种技能的全面测试套件；轻量优化模块专注于优化技能提示及其对应的代码；比较执行模块促进原始和优化技能的执行和评估；可追溯评估模块严格评估性能 against指定标准。Skills-Coach通过虚拟和现实模式提供灵活的执行选项。为了验证其有效性，我们引入Skill-X，一个包含48种多样技能的综合基准数据集。实验结果表明，Skills-Coach在广泛类别中实现了显著的技能能力提升，突显了其在推动更强大和适应性强的LLM代理发展方面的潜力。
英文摘要：
We introduce Skills-Coach, a novel automated framework designed to significantly enhance the self-evolution of skills within Large Language Model (LLM)-based agents. Addressing the current fragmentation of the skill ecosystem, Skills-Coach explores the boundaries of skill capabilities, thereby facilitating the comprehensive competency coverage essential for intelligent applications. The framework comprises four core modules: a Diverse Task Generation Module that systematically creates a comprehensive test suite for various skills; a Lightweight Optimization Module dedicated to optimizing skill prompts and their corresponding code; a Comparative Execution Module facilitating the execution and evaluation of both original and optimized skills; and a Traceable Evaluation Module, which rigorously evaluates performance against specified criteria. Skills-Coach offers flexible execution options through its virtual and real modes. To validate its efficacy, we introduce Skill-X, a comprehensive benchmark dataset consisting of 48 diverse skills. Experimental results demonstrate that Skills-Coach achieves significant performance improvements in skill capability across a wide range of categories, highlighting its potential to advance the development of more robust and adaptable LLM-based agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
