Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis

TL;DR

This paper introduces a comprehensive benchmark and a large dataset for improving GUI grounding, enabling better natural language understanding and manipulation of graphical interfaces, which enhances computer use agents' capabilities.

Contribution

The paper presents OSWorld-G, a detailed benchmark, and Jedi, the largest computer use grounding dataset, advancing the ability to map natural language to complex GUI actions.

Findings

Models trained on Jedi outperform existing approaches on multiple benchmarks.

Grounding improvements significantly enhance agentic performance on complex tasks.

Combining data for different interface elements enables generalization to new interfaces.

Abstract

Graphical user interface (GUI) grounding, the ability to map natural language instructions to specific actions on graphical user interfaces, remains a critical bottleneck in computer use agent development. Current benchmarks oversimplify grounding tasks as short referring expressions, failing to capture the complexity of real-world interactions that require software commonsense, layout understanding, and fine-grained manipulation capabilities. To address these limitations, we introduce OSWorld-G, a comprehensive benchmark comprising 564 finely annotated samples across diverse task types including text matching, element recognition, layout understanding, and precise manipulation. Additionally, we synthesize and release the largest computer use grounding dataset Jedi, which contains 4 million examples through multi-perspective decoupling of tasks. Our multi-scale models trained on Jedi demonstrate its effectiveness by outperforming existing approaches on ScreenSpot-v2, ScreenSpot-Pro, and our OSWorld-G. Furthermore, we demonstrate that improved grounding with Jedi directly enhances agentic capabilities of general foundation models on complex computer tasks, improving from 5% to 27% on OSWorld. Through detailed ablation studies, we identify key factors contributing to grounding performance and verify that combining specialized data for different interface elements enables compositional generalization to novel interfaces. All benchmark, data, checkpoints, and code are open-sourced and available at https://osworld-grounding.github.io.