metadata
license: apache-2.0
datasets:
- PeterJinGo/nq_hotpotqa_train
language:
- en
metrics:
- exact_match
- f1
base_model:
- Qwen/Qwen2.5-3B
SE-Search-3B
Self-Evolving Search Agent via Memory and Dense Reward
🔥 News
- Paper available on [ArXiv]
📖 Overview
SE-Search is a Self-Evolving Search agent that improves online search behavior through a Think-Search-Memorize strategy:
- Memory Purification: Retains salient evidence while filtering irrelevant content
- Atomic Query: Promotes shorter and more diverse queries, improving evidence acquisition
- Dense Rewards: Provides fine-grained feedback that speeds up training and improves performance
🙏 Acknowledgements
Built upon VeRL, Search-R1, and AutoRefine. Thanks to the authors for their valuable work.
🎓 Citations
@misc{li2026sesearch,
title={SE-Search: Self-Evolving Search Agent via Memory and Dense Reward},
author={Jian Li and Yizhang Jin and Dongqi Liu and Hang Ding and Jiafu Wu and Dongsheng Chen and Yunhang Shen and Yulei Qin and Ying Tai and Chengjie Wang and Xiaotong Yuan and Yabiao Wang},
year={2026},
eprint={2603.03293},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2603.03293},
}
@article{li2025survey,
title={A Survey on AI Search with Large Language Models},
author={Li, Jian and Li, Xiaoxi and Zheng, Yan and Jin, Yizhang and Wang, Shuo and Wu, Jiafu and Wang, Yabiao and Wang, Chengjie and Yuan, Xiaotong},
year={2025}
}