GUI-Evolver: Scaling GUI Agents via Data–Model Co-Evolution

We introduce GUI-Evolver, an autonomous data–model co-evolution framework for GUI agents. Student interactions update a persistent State Model Graph (SMG), which supplies continual GUI knowledge and failure-driven remedial tasks. Accumulated knowledge and admitted trajectories train the next student, selected by Validation without per-round human decisions. Teacher-assisted intervention and diagnosis-guided rerollouts provide alternative targets when teacher reasoning is unavailable. Three-evaluation means on Qwen3.5-0.8B/2B/4B/9B and Qwen3.8-27B improve over Base by 9.9–25.0 points on AndroidWorld and 4.8–13.1 on MobileWorld. Controlled comparisons support the contributions of explicit knowledge, targeted practice, and student-compatible supervision within the evaluated settings.