CASU: Case-to-Class Skill Synthesis with Adaptive Skill Utilization for LLM Agents

Large Language Model (LLM) agents increasingly solve complex tasks through interaction, producing trajectories that could benefit future related tasks by preserving successful strategies and failure lessons. Yet this experience often remains underused: raw trajectories are too noisy and instance-specific to become transferable skills, and even when such skills are extracted, it remains unclear how to use them effectively, since some skills are better internalized into model parameters while others are better retained as external guidance. To address these challenges, we introduce CASU, a three-stage framework for hierarchical skill synthesis and adaptive skill utilization that uses only the agent's own experience, without stronger external supervision. Specifically, CASU distills multiple successful and failed trajectories of each task instance into Case Skills, which capture reusable strategies while filtering instance-specific details. It then synthesizes Class Skills from Case Skills collected across tasks in the same category, and optimizes the Class Skill generator with paired environment feedback on held-out tasks. It further proposes Skill Absorption Efficiency (SAE) to estimate the internalization suitability of each learned skill by measuring the consistency of its induced parameter updates across disjoint task subsets. High-SAE skills are internalized through distillation on skill-guided responses, while remaining useful skills are retained for external invocation. Across ALFWorld and WebShop, CASU improves GRPO-trained Qwen2.5-3B and Qwen2.5-7B agents, delivering up to 10.95 points of improvement in ALFWorld average success and 7.0 points in WebShop accuracy, surpassing strong RL, skill-augmented RL, and self-distillation baselines.