1 citations · 2 across the 20 of their papers we have counts for
5 papers · 1 filter
Self-Distilled Agentic Reinforcement Learning
Zhengxi Lu, Zhiyuan Yao, Zhuowen Han +8
Reinforcement learning (RL) has emerged as a central paradigm for post-training LLM agents, yet its trajectory-level reward signal provides only coarse supervision for long-horizon…
UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization
Zhengxi Lu, Fei Tang, Guangyi Liu +8
MLLM-based GUI agents have demonstrated strong capabilities in complex user interface interaction tasks. However, long-horizon scenarios remain challenging, as these agents are bur…
ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents
Fei Tang, Zhiqiong Lu, Boxuan Zhang +4
GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long ta…
UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning
Zhengxi Lu, Jiabo Ye, Fei Tang +8
Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current appro…
GUI-G: Gaussian Reward Modeling for GUI Grounding
Fei Tang, Zhangxuan Gu, Zhengxi Lu +9
Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use…