Infinite-Dreamer：用图像编辑世界模型合成 GUI 智能体训练数据
Imagine to Act: High-Fidelity Data Synthesis via Image Editing World Model for Scalable GUI Agent Training
GUI 智能体缺训练数据的老问题，这篇用图像编辑模型自己造数据，Qwen3-VL 微调后 AndroidWorld 成绩还涨了不少，代码开源可以直接试。
论文提出 Infinite-Dreamer，一种免仿真的数据合成方法，用像素级图像编辑世界模型生成 GUI 截图迁移数据。它把界面变化交给 VLM 描述成结构化 delta-text，再微调图像编辑骨干来合成逼真的截图过渡。仅用合成数据微调 Qwen3-VL 得到的 Infinite-Actor-8B 在 AndroidWorld 上 Pass@1 提升 +4.45，MobileWorld Pass@3 成功率接近翻倍，Infinite-Actor-2B 的 Pass@1 提升 +9.05。代码已在 GitHub 开源。
Imagine to Act: High-Fidelity Data Synthesis via Image Editing World Model for Scalable GUI Agent Training
Graphical User Interface (GUI) agents have emerged as a promising paradigm for automating complex digital workflows across diverse applications. However, training highly capable and generalizable agents fundamentally relies on massive, high-fidelity visual-action trajectories, which are notoriously difficult to acquire. While human demonstrations are unscalable, existing GUI world models rely on text descriptions or HTML rendering, discarding crucial pixel-level visual details like icons and layout styles. To address this issue, we introduce Infinite-Dreamer, a simulation-free data synthesis method powered by a pixel-level Image Editing World Model. By conceptualizing GUI transitions as image editing tasks, we leverage Vision-Language Models (VLMs) to describe action-induced UI changes as structured delta-text. We then fine-tune an image editing backbone to controllably synthesize realistic screenshot transitions. We utilize this model to generate both single-frame visual robustness data and multi-step imaginary trajectories. To validate the effectiveness of our approach, we fine-tune the Qwen3-VL baseline solely on the synthesized data to obtain Infinite-Actor, and evaluate it on AndroidWorld, MobileWorld, and AndroidControl-Curated benchmarks. Infinite-Actor consistently outperforms the Qwen3-VL baselines across scales: Infinite-Actor-8B improves AndroidWorld Pass@1 by +4.45 and nearly doubles the MobileWorld Pass@3 success rate, while Infinite-Actor-2B improves Pass@1 by +9.05. Code is available at https://github.com/swaydy-n/Infinite-Dreamer.