Hypothesis-Driven Skill Optimization for LLM Agents

The article introduces Hypothesis-Driven Skill Optimization (HDSO), a framework designed to enhance action-oriented LLM agents without modifying their weights. HDSO operates with a frozen skill curator and executor, utilizing a structured process of hypothesis generation, validation, and skill approval that leads to performance improvements of +6.9 average success rate points for Qwen3-8B and +4.0 points for Qwen3.6-27B in the ALFWorld environment. This methodology emphasizes a controlled skill lifecycle, which is crucial for practitioners aiming to implement reliable and auditable skill integration in LLMs while mitigating the risks associated with noisy data.