arXiv AI June 17, 2026 By Jingyuan Huang, Zuming Huang, Yucheng Shi, Tianze Yang, Xiaoming Zhai, Wei Chu, Ninghao Liu

Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding

arXiv:2606. 18101v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 12

Test-Time Self-Evolving GUI Visual Grounding via Reflection-Guided On-Policy Self-Distillation

arXiv:2608. 11191v1 Announce Type: cross Abstract: GUI Visual Grounding is a fundamental capability for GUI agents.

By Shiyu Xuan, Zechao Li

agents reinforcement-learning efficiency benchmarks

arXiv AI

Aug 5

When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation

arXiv:2608. 03632v1 Announce Type: new Abstract: On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals.

By Yinuo Jiang, Yongjie Ye, Zhou Tao, Xiang Zhuang, Qiang Zhang, Huajun Chen, Tiankai Li

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Hugging Face Trending Papers

Aug 4

When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation

On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable.

llms efficiency multimodal

arXiv Machine Learning

Aug 11

Adaptive Supervised Anchoring for On-Policy Self-Distillation

arXiv:2608. 07935v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) adapts a language model by distilling guidance from a frozen teacher on trajectories sampled from the student.

By Meilin Yang (Renmin University of China, Beijing, China), Zixuan Ding (Renmin University of China, Beijing, China), Jianhao Nie (Renmin University of China, Beijing, China), Weite Zhang (Renmin University of China, Beijing, China), Yuxin Zhang (Renmin University of China, Beijing, China), Zhiming Shao (Renmin University of China, Beijing, China), Li Yu (Renmin University of China, Beijing, China), Zhe Fu (Renmin University of China, Beijing, China)

llms efficiency benchmarks safety

arXiv Machine Learning

Jul 28

Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation

arXiv:2607. 23125v1 Announce Type: new Abstract: Post-training enables vision-language models (VLMs) to understand human instructions and perform various downstream tasks.

By Shuai Wang, Daoan Zhang, Zhe Tang, Hao Cheng, Jiaheng Wei

llms reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Aug 5

GUI-Lens: Coarse-to-Fine Cropping for GUI Grounding with General-Purpose VLMs

arXiv:2608. 03270v1 Announce Type: cross Abstract: GUI grounding maps natural-language instructions to click locations and is essential for reliable GUI agents.

By Zichuan Fu, Shirong Wang, Wenlin Zhang, Guojing Li, Yimin Deng, Jingtong Gao, Junjia Qi, Hanyu Yan, Yefeng Zheng, Xiaopeng Li, Wanyu Wang, Xian Wu, Xiangyu Zhao

llms agents computer-vision multimodal benchmarks

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