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.

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

🌐 Project Page | 💻 Code | 📖 Paper

👀 Intro

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

For more examples, please refer to our website [🌐 Project Page].

Citation

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

@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}
}