arXiv:2606.14579v1 Announce Type: new Abstract: When applying Group Relative Policy Optimization (GRPO) for GUI Grounding, rollouts are sampled from a single screenshot view; groups often become either all failures on difficult instances or all successes on easy ones, yielding no useful relative advantage. We propose VISTA (View-Consistent Self-Verified Training), a GRPO-based training framework that constructs each comparison group from multiple target-preserving views of the same GUI instance.Each view is generated by a crop that keeps the target element visible and remaps its box exactly, s
The continuous evolution of AI, particularly in GUI grounding and agentic systems, drives the need for more robust and efficient training methods to handle diverse and complex interfaces.
Improved GUI grounding techniques can significantly enhance the reliability and performance of AI agents interacting with digital environments, impacting automation and user experience across industries.
The ability to train AI models with view-consistent, self-verified data makes GUI grounding more resilient to variations and complexities, fostering more capable and generalizable AI agents.
- · AI agent developers
- · Automation software providers
- · Companies with complex digital interfaces
- · Manual testers of digital interfaces
- · Inefficient AI training methodologies
AI agents become more adept at navigating and interacting with various graphical user interfaces.
Increased adoption of AI agents for tasks requiring human-computer interaction, leading to higher automation efficiency.
New forms of software development emerge, prioritizing AI-friendly interface design and automated interaction.
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Read at arXiv cs.AI