UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning

• URL: http://arxiv.org/abs/2509.11543v2
• Date: Wed, 24 Sep 2025 15:05:34 GMT
• Title: UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning
• Authors: Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, Yueting Zhuang,
• Abstract summary: We present Semi-online Reinforcement Learning, a novel paradigm that simulates online RL on offline trajectories.<n>To capture long-term training signals, Semi-online RL introduces discounted future returns into the reward computation.<n>Experiments show that ours Semi-online RL achieves SOTA performance among 7B models across four dynamic benchmarks.
• Score: 78.86567400365392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current approaches face a fundamental dilemma: offline RL enables stable training on pre-collected trajectories, but struggles with multi-step task execution for lack of trajectory-level reward signals; online RL captures these signals through environment interaction, but suffers from sparse rewards and prohibitive deployment costs. To address it, we present Semi-online Reinforcement Learning, a novel paradigm that simulates online RL on offline trajectories. During each rollout process, we preserve the original model output within the multi-turn dialogue, where a Patch Module adaptively recovers the divergence between rollout and expert trajectories. To capture long-term training signals, Semi-online RL introduces discounted future returns into the reward computation and optimizes the policy with weighted step-level and episode-level advantages. We further introduce Semi-Online Performance (SOP), a metric that aligns better with true online performance, serving as a practical and effective proxy for real-world evaluation. Experiments show that ours Semi-online RL achieves SOTA performance among 7B models across four dynamic benchmarks, with significant gains over the base model (e.g., +12.0% on AndroidWorld, +23.8% on AITW), demonstrating significant progress in bridging the gap between offline training efficiency and online multi-turn reasoning. The code is available at https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1.

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