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 generation and evaluates models from multiple dimensions. Based on these resources, we train Gen-Searcher with SFT followed by agentic reinforcement learning with dual reward feedback, which combines text-based and image-based rewards to provide more stable and informative learning signals for GRPO training. Experiments show that Gen-Searcher brings substantial gains, improving Qwen-Image by around 16 points on KnowGen and 15 points on WISE. We hope this work can serve as an open foundation for search agents in image generation, and we fully open-source our data, models, and code.

翻译：近期图像生成模型在生成高保真度与逼真图像方面展现出强大能力。然而，这些模型本质上受限于冻结的内部知识，因此在处理需要密集型知识或实时信息的现实场景时常常表现不佳。本文首次提出Gen-Searcher，通过训练搜索增强的图像生成智能体，使其能够执行多跳推理与搜索，以收集实现基于事实生成所需的文本知识与参考图像。为此，我们构建了专门的数据处理流水线，并整理了两个高质量数据集——Gen-Searcher-SFT-10k与Gen-Searcher-RL-6k，其中包含多样化的搜索密集型提示及其对应的真实合成图像。我们进一步提出了综合基准KnowGen，该基准明确要求基于搜索的外部知识进行图像生成，并从多维度评估模型性能。基于上述资源，我们通过监督微调结合双重奖励反馈的智能体强化学习训练Gen-Searcher，其中文本类与图像类奖励共同为GRPO训练提供更稳定且更具信息量的学习信号。实验表明，Gen-Searcher带来显著性能提升，在KnowGen与WISE基准上分别将Qwen-Image的得分提升约16分与15分。我们期望本工作能为图像生成领域的搜索智能体提供开放基础，并已全面开源数据、模型及代码。