Organizing agent experience into dual-granularity skills (task-level and step-level) with dynamic maintenance significantly improves performance, and these skills transfer across different evaluation settings without major training overhead.
D2Skill creates a dynamic memory system for AI agents that stores two types of reusable skills: high-level task guidance and low-level step-by-step corrections. The system learns from its own training experience, continuously updating and pruning skills based on their usefulness. Tests show 10-20% improvement in task success rates on complex web-based environments.