Introduction

SkillRL-Search-SFT is a cold-start checkpoint for the search RL environment.

Task: Search
Stage: SFT

Key Features

Experience-based Skill Distillation: Transforms successful trajectories into strategic patterns and failed ones into concise lessons from failure.
Hierarchical SKILLBANK: Organizes knowledge into General Skills for universal strategic guidance and Task-Specific Skills for category-level heuristics.
Recursive Skill Evolution: A dynamic mechanism where the skill library co-evolves with the agent's policy during RL by analyzing validation failures.
Context Efficiency: Achieves 10-20% token compression compared to raw trajectory storage while enhancing reasoning utility.

Download

You can download the model then run the training scipts in https://github.com/aiming-lab/SkillRL.

Citation

Model Paper

@article{xia2026skillrl,
  title={SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning},
  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},
  journal={arXiv preprint arXiv:2602.08234},
  year={2026}
}