Gen-Searcher: Reinforcing Agentic Search for Image Generation
Kaituo Feng, Manyuan Zhang, Shuang Chen, Yunlong Lin, Kaixuan Fan, Yilei Jiang, Hongyu Li, Dian Zheng, Chenyang Wang, Xiangyu Yue

TL;DR
Gen-Searcher introduces a search-augmented approach for image generation that incorporates multi-hop reasoning and external knowledge, significantly improving performance on knowledge-intensive tasks.
Contribution
This work is the first to train a search-augmented image generation agent with multi-hop reasoning, curated datasets, and a new benchmark for search-grounded image synthesis.
Findings
Gen-Searcher improves Qwen-Image by about 16 points on KnowGen.
It enhances performance by 15 points on WISE.
The approach combines search, reasoning, and reinforcement learning for better grounded image generation.
Abstract
Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally constrained by frozen internal knowledge, thus often failing on real-world scenarios that are knowledge-intensive or require up-to-date information. In this paper, we present Gen-Searcher, as the first attempt to train a search-augmented image generation agent, which performs multi-hop reasoning and search to collect the textual knowledge and reference images needed for grounded generation. To achieve this, we construct a tailored data pipeline and curate two high-quality datasets, Gen-Searcher-SFT-10k and Gen-Searcher-RL-6k, containing diverse search-intensive prompts and corresponding ground-truth synthesis images. We further introduce KnowGen, a comprehensive benchmark that explicitly requires search-grounded external knowledge for image…
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