OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis

Abstract

Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. Despite their utility in advancing digital automation, the development of such agents faces a critical bottleneck: collecting high-quality trajectory data for training. Common practices for collecting such data rely on human supervision or synthetic data generation through executing pre-defined tasks, which are either resource-intensive or unable to guarantee data quality. Further, these approaches exhibit significant gaps between the generated data and online environments, alongside limited data diversity. To address this issue, we introduce OS-Genesis, a novel GUI data synthesis pipeline that overcomes the challenges above. Unlike prior methods that rely on preset tasks, OS-Genesis reverse engineers the GUI trajectory construction process. Agents first perceive environments and perform step-level interactions, then retrospectively derive high-quality tasks to enable trajectory-level exploration. A trajectory reward model is then employed to ensure the quality of the generated trajectories. We demonstrate that training GUI agents with OS-Genesis significantly improves their performance on highly challenging online benchmarks. In-depth analysis further validates OS-Genesis’s cost-effectiveness and its superior data quality and diversity compared to existing synthesis methods.