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

• URL: http://arxiv.org/abs/2604.27488v1
• Date: Thu, 30 Apr 2026 06:39:37 GMT
• Title: Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO
• Abstract summary: Skills-Coach is an automated framework designed to enhance the self-evolution of skills within Large Language Model (LLM)-based agents.<n>The framework comprises four core modules: a Diverse Task Generation Module that creates a comprehensive test suite for various skills; a Lightweight Optimization Module dedicated to optimizing skill prompts and their corresponding code.<n>To validate its efficacy, we introduce Skill-X, a comprehensive benchmark dataset consisting of 48 diverse skills.
• Score: 31.696187731033334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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