Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation (2506.04614v1)

Abstract: In recent years, Multimodal LLMs (MLLMs) have been extensively utilized for multimodal reasoning tasks, including Graphical User Interface (GUI) automation. Unlike general offline multimodal tasks, GUI automation is executed in online interactive environments, necessitating step-by-step decision-making based on real-time status of the environment. This task has a lower tolerance for decision-making errors at each step, as any mistakes may cumulatively disrupt the process and potentially lead to irreversible outcomes like deletions or payments. To address these issues, we introduce a pre-operative critic mechanism that provides effective feedback prior to the actual execution, by reasoning about the potential outcome and correctness of actions. Specifically, we propose a Suggestion-aware Gradient Relative Policy Optimization (S-GRPO) strategy to construct our pre-operative critic model GUI-Critic-R1, incorporating a novel suggestion reward to enhance the reliability of the model's feedback. Furthermore, we develop a reasoning-bootstrapping based data collection pipeline to create a GUI-Critic-Train and a GUI-Critic-Test, filling existing gaps in GUI critic data. Static experiments on the GUI-Critic-Test across both mobile and web domains reveal that our GUI-Critic-R1 offers significant advantages in critic accuracy compared to current MLLMs. Dynamic evaluation on GUI automation benchmark further highlights the effectiveness and superiority of our model, as evidenced by improved success rates and operational efficiency.

• The paper introduces the GUI-Critic-R1 model that integrates a pre-operative critic using S-GRPO to reduce error rates in GUI automation.
• It employs a reasoning-bootstrapping data collection process with chain-of-thought annotations to train the model on sequential GUI actions.
• Experimental evaluations across mobile and web interfaces show significant improvements in critic accuracy and operational efficiency compared to conventional MLLMs.

Overview of GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation

The paper "Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation" discusses the innovative design of the GUI-Critic-R1 model which addresses the complexities of Graphical User Interface (GUI) automation within online interactive environments. This research focuses on reducing error rates and improving operational efficiency by implementing a pre-operative critic mechanism that evaluates the correctness and potential outcomes of GUI actions before execution.

Need for Pre-Operative Critic in GUI Automation

GUI automation necessitates precise action sequences which, if erroneous, may disrupt entire processes or result in irreversible outcomes such as deletions or unwanted transactions. Given these high stakes, the research introduces a pre-critic model that evaluates the correctness of an agent’s decision by projecting the possible results of actions and generating corrective feedback. This pre-critic mechanism allows GUI agents to make informed decisions, preventing costly mistakes.

Suggestion-aware Gradient Relative Policy Optimization (S-GRPO)

At the core of this paper is the Suggestion-aware Gradient Relative Policy Optimization (S-GRPO), which plays a pivotal role in developing the GUI-Critic-R1 model. S-GRPO incorporates a unique suggestion reward to enhance reasoning capabilities, ensuring the model provides reliable feedback when action errors are anticipated. The methodology demonstrates significant improvements over current Multimodal LLMs (MLLMs), which struggle to autonomously detect errors within critical interactive environments.

Innovative Data Collection Process

The paper emphasizes the creation of robust datasets to train and test the GUI-Critic-R1 model, addressing the existing gaps in GUI pre-critic data. Utilizing a reasoning-bootstrapping based data collection pipeline, researchers compiled high-quality chain-of-thought annotations for training, enabling the model to understand procedural sequences of GUI actions and the interconnectedness of critical decisions without prior knowledge of errors. This meticulous data approach ensures the reliability of the GUI-Critic-R1's judgment capabilities.

Experiments and Results

Through static evaluations on the GUI-Critic-Test across mobile and web interfaces, the GUI-Critic-R1 demonstrated superiority in critic accuracy and operational efficiency when contrasted with established MLLMs. In dynamic GUI automation settings, the model revealed substantial gains in success rates and efficiency, showcasing its effective diagnostic capabilities and reinforcing its applicability in real-world scenarios.

Implications and Future Directions

The implications of this research are multifaceted. In practical applications, the GUI-Critic-R1 model ensures the reliability of GUI automation processes by significantly reducing error rates and enhancing user interactions with software environments. Moreover, this paper paves the way for future developments in AI-driven GUI automation, emphasizing the benefits of pre-operative error diagnosis in complex interactive systems.

The theoretical contributions lie in the novel use of reinforcement learning adaptations within GUI contexts, setting precedence for further investigation into enhancing multimodal models with auxiliary feedback mechanisms. Future research could extend the generalizability of GUI-Critic models to diverse online domains, consolidating their role in advanced AI systems.