--- base_model: - Qwen/Qwen3-VL-8B-Instruct datasets: - GenSearcher/Train-Data library_name: transformers pipeline_tag: image-text-to-text license: apache-2.0 --- # Gen-Searcher-8B Model This repository contains the Gen-Searcher-8B model presented in [Gen-Searcher: Reinforcing Agentic Search for Image Generation](https://arxiv.org/abs/2603.28767). [**Project Page**](https://gen-searcher.vercel.app/) | [**GitHub Repository**](https://github.com/tulerfeng/Gen-Searcher) | [**Paper**](https://arxiv.org/abs/2603.28767) # πŸ‘€ Intro
Gen-Searcher Teaser
We introduce **Gen-Searcher**, as the first attempt to train a multimodal **deep research agent** for image generation that requires complex real-world knowledge. Gen-Searcher can **search the web, browse evidence, reason over multiple sources, and search visual references** before generation, enabling more accurate and up-to-date image synthesis in real-world scenarios. We build two dedicated training datasets **Gen-Searcher-SFT-10k**, **Gen-Searcher-RL-6k** and one new benchmark **KnowGen** for search-grounded image generation. Gen-Searcher achieves significant improvements, delivering **15+ point gains on the KnowGen and WISE benchmarks**. It also demonstrates **strong transferability** to various image generators. All code, models, data, and benchmark are fully released. ## πŸŽ₯ Demo #### Inference Process Example
Inference Process Example
For more examples, please refer to our website [[🌐Project Page]](https://gen-searcher.vercel.app/) ## πŸš€ Training and Inference For detailed instructions on setup, SFT/RL training, and inference, please refer to the [official GitHub repository](https://github.com/tulerfeng/Gen-Searcher). ## πŸ“ Citation If you find our work helpful for your research, please consider citing our work: ```bibtex @article{feng2025gensearcher, title={Gen-Searcher: Reinforcing Agentic Search for Image Generation}, author={Feng, Kaituo and Zhang, Manyuan and Chen, Shuang and Lin, Yunlong and Fan, Kaixuan and Jiang, Yilei and Li, Hongyu and Zheng, Dian and Wang, Chenyang and Yue, Xiangyu}, journal={arXiv preprint arXiv:2603.28767}, year={2025} } ```