Vision-Language Models (VLMs) have catalyzed the development of Graphical User Interface (GUI) agents for desktop automation. However, reinforcement learning (RL) for GUI agents remains bottlenecked by two challenges: existing RL frameworks still leave substantial idle time inside the rollout stage, and the learning signal from long-horizon, sparse-reward interactions is highly imbalanced. In this paper, we introduce DART, a fully Decoupled Agentic RL Training framework for GUI agents. Whereas prior RL designs only separate the trainer from the rollout, DART splits the pipeline into four independent modules: environment cluster, rollout service, data manager, and trainer, which brings two extra efficiency benefits: rollout-wise trajectory scheduling and per-worker model synchronization. This yields a 1.6× increase in GPU utilization, a 1.9× boost in training through put, and a 5.5× increase in environment utilization over a non-decoupled baseline. We further propose a multi-level adaptive data curation scheme operating at the task, trajectory, step, and token levels: it reallocates rollout budget by task difficulty, bootstraps hard tasks with a pre-collected experience pool, restricts policy updates to high-entropy critical steps, and applies truncated importance sampling to absorb the distribution gap between the rollout and training engines. On the OSWorld benchmark, our DART-GUI-7B achieves a 43.28% task success rate, a 15.76% absolute improvement over its base model and a new state-of-the art among open-source 7B-scale agents. Code, data, and model checkpoints are open-sourced at https://computer-use-agents.github.io/dart-gui/.