WM-R1: Training GUI Agents to Reason and leverage World Models with Reinforcement Learning

arXiv cs.AIAugust 31, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2608.27508v1 Announce Type: new Abstract: GUI agents trained with reinforcement learning (RL) have showcased strong environment learning capabilities on mobile platforms. However, RL typically demands extensive real-environment interactions, leading to high resource costs and instability, especially in GUI scenarios. To address these, we propose WM-R1, the first reinforcement learning framework that trains mobile GUI agents with world models instead of real environments. Specifically, world models serve as the source of state transitions during all rollouts, replacing the real Android environment within the training loop. WM-R1 also embeds world models directly into the thinking proces

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