Designing GUI agents that perform human-like tasks on graphical user interfaces faces a critical obstacle: collecting high-quality trajectory data for training. Existing methods depend on expensive and time-consuming human supervision or on generating synthetic data, which can hardly reflect the diversity and dynamics in the real world. Those constraints significantly limit the GUI agents’ scalability and effectiveness and prevent them from acting autonomously and adapting to diverse and dynamic environments.
Traditional data acquisition for GUI agents is generally based on task-oriented methods. Human annotation is a labor-intensive process that involves designing tasks and annotating trajectories. Although synthetic data reduces the dependency on humans, it depends on pre-defined high-level tasks, which limit the scope and scale of the data. The presence of errors in the intermediate steps or conflicting objectives in the task results in incoherent trajectories and thus decreases the quality of the training data. As mentioned above, these restrictions limit the generalization abilities of agents to work effectively in dynamic or unfamiliar environments.
Researchers from Shanghai AI Laboratory, The University of Hong Kong, Johns Hopkins University, Shanghai Jiao Tong University, the University of Oxford, and Hong Kong University of Science and Technology propose OS-Genesis, a groundbreaking strategy to address these challenges through interaction-driven reverse task synthesis. Unlike predetermined tasks, the exploratory mode of GUI agents involves interaction through clicks, scrolling, and typing over GUI elements for environments. In a retrospective analysis, these interactions are transformed into low-level instructions and contextualized as high-level tasks. It maintains data quality through a TRM, by scoring synthesized trajectories along dimensions of coherence, logical flow, and completeness. Even partial but meaningful data can be trained in such an approach. By bridging the gap between abstract instructions and the dynamic nature of GUIs, this framework significantly enhances the quality and diversity of training data while eliminating the need for human supervision.
The OS-Genesis process consists of several integral components. First, the system autonomously explores dynamic GUI elements, recording transitions between pre- and post-action states to collect foundational data for task synthesis. These transitions are then transformed into detailed low-level instructions with the help of models like GPT-4o. Those instructions are incorporated into comprehensive high-level objectives related to the overall intention of the users, thereby attaining semantic depth. The synthesized pathways then undergo evaluation via the Trajectory Reward Model which uses a stratified scoring framework that focuses more on aspects of logical coherence as well as effective task completion. This ensures the diversity and high quality of data, thus providing a strong basis for training.
Extensive experiments were conducted using benchmarks like AndroidWorld and WebArena, which mimic complex and dynamic environments. Vision-language models, namely Qwen2-VL and InternVL2, were used as the base frameworks for the training process. The training focused on improving both sophisticated task planning and precise low-level action execution to enable deep skill learning for GUI agents.
OS-Genesis was successfully validated on a variety of benchmarks. On AndroidWorld, success rates nearly doubled those of task-driven methods regarding the ability to improve task planning and execution. On AndroidControl, the method performed very well at the high level of autonomous planning but also at the low level of step-by-step execution, including out-of-distribution examples; this shows robustness. On WebArena, the approach outperformed traditional baselines consistently, thereby gaining ground in handling complex and interactive environments. In summary, these results demonstrate the ability of OS-Genesis to generate high-quality trajectories of all sorts, thereby greatly improving the overall effectiveness of GUI agents in general situations.
OS-Genesis is a revolutionary step in the training of GUI agents, as it overcomes the limitations of current data collection methods. Its interaction-driven methodology and reward-based evaluation ensure high-quality and diverse training data that bridge the gap between abstract task instructions and dynamic GUI environments. This approach opens the way for significant progress in digital automation and AI research by enabling GUI agents to learn and adapt autonomously.
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