WM-R1 Trains Mobile GUI Agents with World Models

Summary

Researchers introduce WM-R1, a reinforcement-learning framework that trains mobile GUI agents with world models instead of real Android environments. World models generate state transitions during rollouts and help agents reason about candidate action consequences before execution. The approach removes real-environment interaction, supports massively parallel and step-level trajectory generation, and uses a multi-dimensional rule-based reward for task success, efficiency, and world-model utilization. On Android benchmarks, agents trained with WM-R1 significantly outperform GRPO-only baselines and inference-time simulation methods. The training dataset contains 2,000 challenging tasks, and the implementation is publicly available.