LiteGUI: Distilling Compact GUI Agents with Reinforcement Learning

Developing lightweight, on-device vision-language GUI agents is essential for efficient cross-platform automated interaction. However, current on-device agents are constrained by limited model capacity, and further performance improvements remain urgently needed. Traditional Supervised Fine-Tuning (SFT) for small-scale models often leads to overfitting, catastrophic forgetting and policy rigidity, and thus fails to fully address these challenges. In this work, we propose a novel SFT-free trainin

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ethics.ai (8 May 2026), “LiteGUI: Distilling Compact GUI Agents with Reinforcement Learning,” evidence record 4746, https://ethics.ai/record/4746 (originally published by arXiv).

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