Author

Zhi-Feng Li

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#artificial intelligence Preprint Oct 2026

GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning

The Distillation-to-Reinforcement Learning (D2RL) method is proposed to address the limited generalization of supervised fine-tuning and the sparse reward problem in reinforcement learning, and produces substantial gains over models trained on open-source datasets, with performance comparable or even superior to models...

Qing-Song Xie, Jia-Hao Tian, Jia-Cheng Tang et al. · 0 citations

#machine learning Preprint Sep 2026

Train What You Deploy: Closing the MLP Reachability Gap in Low-Rank Clone Distillation

This work shows that a state-of-the-art weight-inheritance distiller, Low-Rank Clone (LRC), deploys a full-width student MLP but ties training to a teacher-induced slice, leaving 62.5-81.4% of each deployed matrix's independent linear degrees of freedom unreachable-paid for at inference, never trainable.

Wen-Hui Chen, Zhi-Feng Li, Jie Zhou et al. · 0 citations