The paper delivers the first comprehensive overview of RL for GUI agents, organizing methods into offline, online, and hybrid strategies while analyzing trends in rewards, efficiency, and deliberation to outline a future roadmap.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
SynthAgent uses dual refinement of synthetic tasks and trajectories to produce higher-quality training data that improves web agent adaptation to target environments.
citing papers explorer
-
GUI Agents with Reinforcement Learning: Toward Digital Inhabitants
The paper delivers the first comprehensive overview of RL for GUI agents, organizing methods into offline, online, and hybrid strategies while analyzing trends in rewards, efficiency, and deliberation to outline a future roadmap.
-
SynthAgent: Adapting Web Agents with Synthetic Supervision
SynthAgent uses dual refinement of synthetic tasks and trajectories to produce higher-quality training data that improves web agent adaptation to target environments.
- Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond