GUI-Shepherd: Reliable Process Reward and Verification for Long-Sequence GUI Tasks

Cong Chen; Kaixiang Ji; Hao Zhong; Muzhi Zhu; Anzhou Li; Guo Gan; Ziyuan Huang; Cheng Zou; Jiajia Liu; Jingdong Chen; Hao Chen; Chunhua Shen

arXiv · 2509.23738

GUI-Shepherd: Reliable Process Reward and Verification for Long-Sequence GUI Tasks

Cong Chen·Kaixiang Ji·Hao Zhong·Muzhi Zhu·Anzhou Li·Guo Gan·Ziyuan Huang·Cheng Zou·Jiajia Liu·Jingdong Chen·Hao Chen·Chunhua Shen

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Abstract

Autonomous agents for long-sequence Graphical User Interface tasks are hindered by sparse rewards and the intractable credit assignment problem. To address these challenges, we introduce GUI-Shepherd, a Process Reward Model that provides dense, step-by-step feedback to guide agents. GUI-Shepherd is trained on a diverse large-scale data set of $52$k interactions that features human-annotated scores and GPT-4o generated rationales, enabling it to serve both as a reward provider for RL training and as a verifier for inference. As far as we know, we are the first to conduct a systematic study of process supervision in GUI agents, across diverse settings from online long-horizon tasks to offline single-step prediction. On the online AndroidWorld benchmark, GUI-Shepherd improves success rate by $7.7$ points via multi-turn online PPO, significantly outperforming Outcome Reward Model based competitors. When used as an inference verifier, it brings $5.1$ points improvements. The benefits generalize to the offline AndroidControl benchmark, with gains of $2.2$ points as a reward provider and $4.3$ points as a verifier. Collectively, our results establish that high-fidelity process supervision is critical for building more capable GUI agents and present a generalizable solution.

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Cong Chen, Kaixiang Ji, Hao Zhong, Muzhi Zhu, Anzhou Li, Guo Gan, Ziyuan Huang, Cheng Zou, Jiajia Liu, Jingdong Chen, Hao Chen, Chunhua Shen. 2025-09-28. GUI-Shepherd: Reliable Process Reward and Verification for Long-Sequence GUI Tasks. https://arxiv.org/abs/2509.23738

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