ProRe: A Proactive Reward System for GUI Agents via Reasoner–Actor Collaboration

Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods struggle to generalize to GUI agents, where access to ground-truth trajectories or application databases is often unavailable, and static trajectory-based LLM-as-a-Judge approaches suffer from limited accuracy. To address these challenges, we propose PRORE, a proactive reward system that leverages a general-purpose reasoner and domain-specific evaluator agents (actors). The reasoner schedules targeted state probing tasks, which the evaluator agents then execute by actively interacting with the environment to collect additional observations. This enables the reasoner to assign more accurate and verifiable rewards to GUI agents. Empirical results on over 3K trajectories demonstrate that PRORE improves reward accuracy and F1 score by up to 5.3% and 19.4%, respectively. Furthermore, integrating PRORE with state-of-the-art policy agents yields a success rate improvement of up to 22.4%. The source code is available at https://github.com/V-Droid-Agent/ ProRe . INTRODUCTION Verifiable rewards are pivotal for enabling the continual evolution of large language model (LLM)based agents Wang et al. (2024b); Guo et al. (2025); Silver & Sutton (2025). Within this paradigm, LLMs operate as policy networks, undertaking user requests to generate reasoning, invoke tools and functions, and manipulate graphical user interfaces (GUIs) Qi et al. (2024). Rewards function as quantitative feedback signals that steer the agent's learning process Gao et al. (2024), promoting optimal behaviors while discouraging suboptimal actions. Reinforcement learning with verifiable rewards (RLVR) has the potential to significantly advance GUI agents Wang et al. (2024c); Xu et al. (2025); Wang et al. (2025). A simple yet effective binary reward for GUI automation is to assess whether the specified task has been successfully completed. To obtain such a reward signal, existing methodologies could be generally categorized into rule-based and LLM-based, as illustrated in Figure 1 . In the rule-based paradigm, human experts manually construct verification code snippets to ascertain the realization of the intended state for each task. For instance, AndroidWorld Rawles et al. (2024) and WindowsAgentArena Bonatti et al. (2024) datasets contain more than 116 and 150 manually engineered unit testing code, respectively, to provide grounded signals of task accomplishment for individual GUI automation tasks. While this approach offers high accuracy, it is inherently limited in scalability, as the manual creation of unit testing scripts demands substantial human effort and resources, thereby preventing its use as a reward mechanism for large-scale GUI agent training. LLM-as-a-judge is thus proposed to enable scalable agentic rewards Gu et al. (2024); Bai et al. (2024) . Leveraging the capabilities of advanced LLMs such as GPT-4o, this approach evaluates GUI task trajectories, often represented as screenshots, by prompting the model with queries such