Paper Detail
Jingyuan Huang, Zuming Huang, Yucheng Shi, Tianze Yang, Xiaoming Zhai, Wei Chu, Ninghao Liu
Graphical user interface (GUI) grounding requires vision-language models (VLMs) to identify small target elements in high-resolution screenshots and predict precise screen coordinates. On-policy self-distillation (OPSD) is a promising post-training approach for this coordinate-sensitive task, since it provides dense token-level teacher signals beyond hard coordinate labels. However, naive OPSD is not well suited to GUI grounding: OPSD evaluates the teacher on student-generated prefixes, the quality of coordinate-token teacher signals can degrade when the prefix has already deviated from the target coordinate, leading to unreliable teacher signal. To mitigate this, We propose quality-aware self-distillation for VLM-based GUI grounding, which improves coordinate-token teacher-signal quality through soft correctness-aware gating and teacher-probability scaling. The soft correctness-aware gate checks whether the teacher's current coordinate-token prediction can still be completed into the ground-truth box under the student-generated prefix. If not, the corresponding teacher signal is down-weighted. Teacher-probability scaling then uses the teacher's confidence as a lightweight factor to further calibrate the strength of the gated supervision. A key empirical finding is that neither component alone improves overall performance, whereas combining them consistently improves performance. This suggests that the two mechanisms play complementary roles: correctness-aware gating suppresses unreliable coordinate-token supervision, while teacher-probability scaling calibrates the strength of the remaining signals. Experiments across six GUI grounding benchmarks show that our method consistently improves the base model and outperforms strong baselines.
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@misc{huang2026trust,
title = {Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding},
author = {Jingyuan Huang and Zuming Huang and Yucheng Shi and Tianze Yang and Xiaoming Zhai and Wei Chu and Ninghao Liu},
year = {2026},
abstract = {Graphical user interface (GUI) grounding requires vision-language models (VLMs) to identify small target elements in high-resolution screenshots and predict precise screen coordinates. On-policy self-distillation (OPSD) is a promising post-training approach for this coordinate-sensitive task, since it provides dense token-level teacher signals beyond hard coordinate labels. However, naive OPSD is not well suited to GUI grounding: OPSD evaluates the teacher on student-generated prefixes, the qual},
url = {https://huggingface.co/papers/2606.18101},
keywords = {vision-language models, on-policy self-distillation, coordinate-sensitive task, dense token-level teacher signals, soft correctness-aware gating, teacher-probability scaling, GUI grounding, screen coordinates, huggingface daily},
eprint = {2606.18101},
archiveprefix = {arXiv},
}
{}