技能教练：基于免训练GRPO的自我进化式技能优化器

摘要

我们推出Skills-Coach——一种创新的自动化框架，旨在显著增强基于大语言模型（LLM）智能体的技能自我进化能力。针对当前技能生态系统的碎片化现状，该框架通过探索技能能力的边界，为智能应用实现全面能力覆盖提供支撑。该体系包含四大核心模块：多样化任务生成模块系统化构建覆盖各类技能的综合测试集；轻量化优化模块专注于技能提示词及对应代码的优化；对比执行模块实现原始技能与优化后技能的并行执行与评估；可追溯评估模块则依据既定标准对技能表现进行严格评判。Skills-Coach通过虚拟与真实两种模式提供灵活的执行方案。为验证其有效性，我们同步推出包含48项多元化技能的基准数据集Skill-X。实验结果表明，该框架在多种技能类别上均实现了显著的性能提升，彰显了其在推动构建更强健、自适应LLM智能体方面的潜力。

English

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.

技能教练：基于免训练GRPO的自我进化式技能优化器

Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO

摘要

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