Dynamic Dual-Granularity Skill Bank for Agentic RL

📰 ArXiv cs.AI

D2Skill is a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills and step skills

advanced Published 31 Mar 2026

Action Steps

Organize reusable experience into task skills for high-level guidance
Extract step skills for fine-grained decision support and error correction
Implement D2Skill in agentic RL frameworks to improve performance and efficiency
Evaluate and refine D2Skill through experimentation and analysis

Who Needs to Know This

ML researchers and AI engineers on a team can benefit from D2Skill as it provides a principled mechanism for maintaining an evolving skill memory, enabling more efficient and effective agentic RL

Key Insight

💡 D2Skill provides a principled mechanism for maintaining an evolving skill memory, enabling more efficient and effective agentic RL

Key Takeaways

D2Skill is a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills and step skills

Full Article

Title: Dynamic Dual-Granularity Skill Bank for Agentic RL

Abstract:
arXiv:2603.28716v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error co

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