SkillRL: Recursive Skill-Augmented Reinforcement Learning Achieves 15.3% Agent Improvement

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A framework where LLM agents automatically discover reusable behavioral patterns from experience through recursive skill-augmented RL. Achieves 15.3% improvement over memory-based agent-tuning baselines on ALFWorld, WebShop, and search-augmented benchmarks. Model checkpoints released on HuggingFace — a concrete implementation of the 'agents that improve themselves' paradigm.