The paper surveys the use of reinforcement learning (RL) in graphical user interface (GUI) agents, outlining how RL addresses challenges such as long-horizon credit assignment, distribution shifts, and safe exploration that supervised fine-tuning alone cannot solve. It introduces a taxonomy dividing methods into Offline RL, Online RL, and Hybrid Strategies, and discusses key aspects like reward engineering, data efficiency, and technical innovations. Emerging trends highlighted include the move toward composite, multi-tier reward architectures, world-model-based training to mitigate GUI I/O latency, and the spontaneous appearance of System‑2‑style deliberation, all of which inform a roadmap for future robust GUI automation.
arXiv:2506. 17913v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents have made significant progress in automating digital tasks through the utilization of computer vision and language models.
arXiv:2606. 24515v1 Announce Type: new Abstract: Computer-Use Agents (CUAs) execute high-level user goals by perceiving and acting directly within graphical user interfaces.
MAI-Gym is an open, all-in-one platform that unifies GUI agent evaluation, data collection, and online reinforcement learning training across mobile, computer, and web environments. It supports ten established benchmarks, scales to 20,000 parallel environment instances, and enables automated synthesis of diverse GUI applications and tasks with execution-validated verifiers. Using this platform, the authors trained a 9B-parameter agent, MAI-Gym-9B, achieving state‑of‑the‑art performance on MobileWorld and OSWorld-Verified, and demonstrated that training on a mocked train‑ticketing app improves real‑world task success from 28.8% to 48.1% on a physical phone.
arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly...
arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
UI‑Venus‑2 is a general‑purpose foundation GUI agent that operates across mobile, web, and desktop environments using a unified closed‑loop reasoning‑action framework. The report details how the system expands environment coverage to over 170 multilingual mobile apps and native desktop OSes, scales task generation through a deep‑research pipeline, and enhances verification with trace‑level and sample‑level evaluators that use visual keypoints and multi‑model voting. Safety‑aware mechanisms are also incorporated to control consequential actions, positioning UI‑Venus‑2 as an efficient, open‑source tool for more generalizable, verifiable, and self‑reflective agents in real‑world applications.
arXiv:2607. 25904v1 Announce Type: new Abstract: Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction.
WebHarbor is a collection of 153 dockerized website environments that faithfully replicate real-world web services, offering 3,518 tasks that include multi-step and post-authentication workflows. Built through task-driven human-agent collaboration, the suite ensures deterministic control, visual fidelity, and functional correctness. The platform enables reliable evaluation and cost-efficient reinforcement learning, with a Qwen 4B-level GUI agent trained in WebHarbor generalizing well to unseen tasks and matching leading open-source agents on four benchmarks.
arXiv:2609.39547v1 Announce Type: new Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GU...
arXiv:2606. 29705v1 Announce Type: new Abstract: Data, as the fundamental substrate of modern intelligence, has greatly driven the development of current foundation models.
LiteGUI is a new framework for training lightweight GUI agents that tackles the long‑horizon, multi‑path nature of GUI interactions. It introduces a two‑stage post‑training pipeline: Guided On‑Policy Distillation, which uses human‑verified multi‑solution action annotations to provide privileged teacher guidance at each state, and Multi‑Solution Dual‑Level GRPO, which combines action‑level supervision with history‑conditioned planning‑quality supervision while handling multiple valid actions. The authors also present a scalable data generation pipeline and the Lite‑Dataset, enabling extensive evaluation across GUI benchmarks and demonstrating substantial performance gains over existing training paradigms.
The paper introduces MENTOR, a multi‑agent framework that provides on‑policy pedagogical intervention to address zero‑reward collapse in reinforcement learning from verifiable rewards. MENTOR diagnoses failures in real time and generates targeted prompts to guide the learning agent, integrating seamlessly with existing policy optimization pipelines such as GRPO. Experiments on math and code generation tasks show that MENTOR significantly reduces collapse, achieving up to 78.3% relative gains on Llama‑3.2‑3B‑Instruct.