---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
---

# Gen-Searcher SFT Model

This repository contains the Supervised Fine-Tuning (SFT) model presented in the paper: [Gen-Searcher: Reinforcing Agentic Search for Image Generation](https://arxiv.org/abs/2603.28767).

This is an intermediate model prepared for subsequent reinforcement learning (RL) training using the GRPO algorithm with dual reward feedback.

[**🌐 Project Page**](https://gen-searcher.vercel.app/) | [**💻 Code**](https://github.com/tulerfeng/Gen-Searcher) | [**📖 Paper**](https://arxiv.org/abs/2603.28767)

# 👀 Intro

<div align="center">
  <img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/teaser.jpg?raw=true" alt="Gen-Searcher Teaser" width="80%">
</div>

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.

## 🎥 Demo

#### Inference Process Example

<div align="center">
  <img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/example.jpg?raw=true" alt="Inference Process Example" width="85%">
</div>

For more examples, please refer to our website [[🌐 Project Page]](https://gen-searcher.vercel.app/).

## Citation

If you find our work helpful for your research, please consider citing our work:

```bibtex
@article{feng2026gen,
  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={2026}
}
```