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Experience-based Skill Distillation: Transforms successful trajectories into strategic patterns and failed ones into concise lessons from failure.
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Hierarchical SKILLBANK: Organizes knowledge into General Skills for universal strategic guidance and Task-Specific Skills for category-level heuristics.
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Recursive Skill Evolution: A dynamic mechanism where the skill library co-evolves with the agent's policy during RL by analyzing validation failures.
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Context Efficiency: Achieves 10-20% token compression compared to raw trajectory storage while enhancing reasoning utility.
You can download the model then run the training scipts in
https://github.com/aiming-lab/SkillRL.
1@article{xia2026skillrl,
2 title={SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning},
3 author={Xia, Peng and Chen, Jianwen and Wang, Hanyang and Liu, Jiaqi and Zeng, Kaide and Wang, Yu and Han, Siwei and Zhou, Yiyang and Zhao, Xujiang and Chen, Haifeng and others},
4 journal={arXiv preprint arXiv:2602.08234},
5 year={2026}
6}
