Junan Hu, Jian Liu, Jingxiang Lai, Jiarui Hu, Yiwei Sheng, Shuang Chen, Jian Li, Dazhao Du 等 9 位作者
Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces visually. Yet supervised fine-tuning alone cannot handle long-horizon credit assignment, distribution shifts, and safe exploration in irreversible environments, making Reinforcement Learning (RL) a central methodology for advancing automation. In this work, we present the first comprehensive overview of the intersection between RL and GUI agents, and examine how this research direction may evolve toward digital inhabitants. We propose a principled taxonomy that organizes existing methods into Offline RL, Online RL, and Hybrid Strategies, and complement it with analyses of reward engineering, data efficiency, and key technical innovations. Our analysis reveals several emerging trends: the tension between reliability and scalability is motivating the adoption of composite, multi-tier reward architectures; GUI I/O latency bottlenecks are accelerating the shift toward world-model-based training, which can yield substantial performance gains; and the spontaneous emergence of System-2-style deliberation suggests that explicit reasoning supervision may not be necessary when sufficiently rich reward signals are available. We distill these findings into a roadmap covering process rewards, continual RL, cognitive architectures, and safe deployment, aiming to guide the next generation of robust GUI automation and its agent-native infrastructure.
Yaowu Fan, Tao Han, Dazhao Du, Andy J. Ma, Jia Wan
Recent advances in computer vision have produced powerful specialized models for detection, segmentation, counting, and other visual tasks. However, these models are typically optimized for isolated tasks, limiting their ability to support general-purpose visual intelligence, particularly when complex language understanding and dense small-object perception are required. We propose VisHarness, a trainable visual agent that decouples high-level perception, reasoning, and decision-making from low-level task execution. Rather than training a task-specific model, we train VisHarness to harness a set of carefully designed heterogeneous visual experts. This paradigm preserves the agent's general intelligence while leveraging the precision of specialized visual experts for specific tasks. With only lightweight training, VisHarness learns a generalizable policy for harnessing visual experts, enabling it to solve fundamental vision tasks in diverse complex scenarios through multi-turn interactions with these experts. To enable efficient on-policy reinforcement learning in a live environment, we introduce dynamic visual memory archiving to reduce the visual-token overhead that rapidly accumulates during multi-turn interactions with visual experts. Experiments on four representative benchmarks spanning reasoning segmentation, generalized referring expression segmentation, dense small-object detection, and referring expression counting show that VisHarness substantially outperforms existing general-purpose models and is competitive with or superior to task-specific models. Our code, model weights, and training data are publicly available at https://github.com/fyw1999/VisHarness.