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

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, a critical bottleneck persists: 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. Moreover, these methods suffer from limited data diversity and significant gaps between synthetic data and real-world environments. To address these challenges, we propose OS-Genesis, a novel GUI data synthesis pipeline that reverses the conventional trajectory collection process. Instead of relying on pre-defined tasks, OS-Genesis enables agents first to perceive environments and perform step-wise 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 efficiency and its superior data quality and diversity compared to existing synthesis methods. Our codes, data, and checkpoints are available at OS-Genesis Homepage. 2 We build dynamic environments on the basis of Zhou et al. (2024) and Rawles et al. (2024). TRM High: Mark the 'Avocado Toast with Egg' recipe as a favorite in the Broccoli app. High: Set a reminder for the 'Review session for Annual Report' scheduled on October 18th in Simple Calendar Pro and save the changes. Low: I need to first open the Broccoli-Recipe App. Action: OpenApp ("Broccoli") Low: Click on Avocado Toast with Egg to view more details.