Efficient Multi-turn RL for GUI Agents via Decoupled Training and
  Adaptive Data Curation
Authors: name: Pengxiang Li, name: Zechen Hu, name: Zirui Shang, name: Jingrong Wu, name: Yang Liu, name: Hui Liu, name: Zhi Gao, name: Chenrui Shi, name: Bofei Zhang, name: Zihao Zhang, name: Xiaochuan Shi, name: Zedong YU, name: Yuwei Wu, name: Xinxiao Wu, name: Yunde Jia, name: Liuyu Xiang, name: Zhaofeng He, name: Qing Li
Organization: Beijing Institute for General Artificial Intelligence

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. We will fully open-source our training framework, data, and
model checkpoints via computer-use-agents.github.io/dart-gui, which we believe
is a timely contribution to the open-source community of agentic RL training.