GAIA: A Data Flywheel System for Training GUI Test-Time Scaling Critic Models

While Large Vision-Language Models (LVLMs) have signifi-cantly advanced GUI agents’ capabilities in parsing textual instructions,interpreting screen content, and executing tasks, a critical challenge per-sists: the irreversibility of agent operations—where a single erroneous ac-tion can trigger catastrophic deviations. To address this, we propose theGUI Action Critic’s Data Flywheel System (GAIA), a training frame-work that enables the models to have iterative critic capabilities, whichare used to improve the Test-Time Scaling (TTS) of basic GUI agents’performance. Specifically, we train an Intuitive Critic Model (ICM)using positive and negative action examples from a base agent first. Thiscritic evaluates the immediate correctness of the agent’s intended actions,thereby selecting operations with higher success probability. Then, theinitial critic guides agent actions to collect refined positive/negative sam-ples, initiating the self-improving cycle. The augmented data then trainsa second-round critic with enhanced discernment capability. We conductexperiments on various datasets and demonstrate that the proposed ICMcan improve the test-time performance of various closed-source and open-source models, and the performance can be gradually improved as thedata is recycled. The code, dataset, and accompanying datasheet will bepublicly released at https://github.com/SeerRay-Lab/GAIA.