arXiv cs.AI· Yuxiang Chai, Quyu Kong, Panrong Tong, Steven Hoi, Yue Wang·· 14 小时前AI 评分38
面向 GUI 智能体的高效自适应训练：在哪里学、回滚多远
Where to Learn and How Far to Roll Out: Efficient Adaptive Training for GUI Agents
AI 导读
一种两阶段自适应训练框架通过从专家轨迹构建可复用中间检查点、用动作分歧挑选信息量高的枢轴点，让 GUI 智能体只在关键状态学习并按任务进度逐步扩展回滚范围。在 MobileGym 和 CUA-Gym-Hub 上，该方法相比 Vanilla GRPO 减少 56-61% 的环境交互，同时提升任务表现。
正文 · 原文
Abstract:Trajectory-level GRPO is a common approach for training multi-step GUI agents, but repeatedly rolling out complete trajectories from the task root can be inefficient: task-level rewards provide coarse supervision over which intermediate decisions require improvement, while already-mastered prefixes are repeatedly re-executed before reaching difficult states. We introduce a two-stage adaptive training framework that jointly addresses where to learn and how far to roll out. We first construct reusable intermediate checkpoints from successful expert trajectories and select informative pivots using action disagreement, which measures uncertainty directly from sampled GUI actions. Stage I performs one-step RL at these pivots to improve uncertain local decisions. Stage II then progressively expands policy responsibility through adaptive Local, Suffix, and Root training according to task progress. Recoverable checkpoints enable repeated training from intermediate states without replaying the preceding trajectory. Across MobileGym and CUA-Gym-Hub, our method reduces environment interaction by 56-61% relative to Vanilla GRPO while improving task performance, demonstrating that jointly allocating training across states and rollout ranges can substantially improve GUI-agent RL efficiency.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39251 [cs.AI] |
| (or arXiv:2610.39251v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39251 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuxiang Chai [view email]
[v1]
Thu, 29 Oct 2026 03:03:29 UTC (1,880 KB)
来源：arXiv cs.AI · arxiv.org