Title: Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: LLM, GUI Agents, RLVR

Abstract: Vision-language model (VLM) based GUI agents show promise for automating complex desktop and mobile tasks, but face significant challenges in applying reinforcement learning (RL): (1) slow multi-turn interactions with GUI environments for policy rollout, and (2) insufficient high-quality agent-environment interactions for policy learning.
To address these challenges, we propose **DART**, a Decoupled Agentic RL Training framework for GUI agents, which coordinates heterogeneous modules in a highly decoupled manner. DART separates the training system into four asynchronous modules: environment cluster, rollout service, data manager, and trainer. This design enables non-blocking communication, asynchronous training, rollout-wise trajectory sampling, and per-worker model synchronization, significantly improving the system efficiency: *1.6× GPU utilization for rollout*, *1.9× training throughput*, and *5.5× environment utilization*.
To facilitate effective learning from abundant samples, we introduce an adaptive data curation scheme: (1) pre-collecting successful trajectories for challenging tasks to supplement sparse success in online sampling; (2) dynamically adjusting rollout numbers and trajectory lengths based on task difficulty; (3) training selectively on high-entropy steps to prioritize critical decisions; (4) stabilizing learning via truncated importance sampling for policy mismatch between policy rollout and updating.
On the OSWorld benchmark, DART-GUI-7B achieves a 42.13% task success rate, a 14.61% absolute gain over the base model, and 7.34% higher than open-source SOTA.
To ensure reproducibility, ***we will fully open-source our training framework, data, and model checkpoints via*** [https://iclr26-dart-gui.github.io](https://iclr26-dart-gui.github.io) (anonymous), which we believe is a timely contribution to the open-source community of agentic RL training.