Title: Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation
Venue: NeurIPS 2025
Accepted: yes
Keywords: GUI agent

critic models

RL

GRPO
Abstract: In recent years, Multimodal Large Language Models (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 the 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 Group 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 code is available at https://github.com/X-PLUG/MobileAgent/tree/main/GUI-Critic-R1.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a pre-operative critic mechanism for MLLM-based GUI agents. The critic can provide potential errors and suggestions for an action before execution. To train the critic model, the authors propose Suggestion-aware Group Relative Policy Optimization (S-GRPO). It contains a suggestion award besides a right or wrong signal. Besides, a data collection pipeline called reasoning bootstrapping is also developed for critic training and testing. Evaluation results show priority of this method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The idea of a pre-operative critic is novel and useful for GUI agent development.\n- The paper is well-written and logically organized. The method and results are presented clearly.\n- The dataset of GUI-Critic-Train and GUI-Critic-Test offers data support for future research on GUI agent critics.\n\nWeaknesses:\nPlease refer to the questions.\n\nQUESTIONS:\n1. The experiment for critic evaluation is quite sufficient, however, the main experiment of dynamic evaluation is mainly conducted on Android environments. Is this method also suitable for more broader scenarios such as desktop applications? \n2. It requires high real-time abilities for online GUI agents, so how efficient the critic is? \n- In Sec. 4.2.2, the authors introduce a metric that measures the proportion that the ‘baseline + critic’ model achieves fewer steps than the baseline, called Efficiency Advantage Rate (EAR).  However, besides step number, the token number and latency are also factors that influence efficiency. If the critic tends to generate long suggestions, the task execution time may be longer. \n- Besides, for samples where the 'baseline+critic' model generates more steps than the baseline, the EAR metric does not evaluate how worse they are. So why choosing the EAR metric rather than computing average step numbers?\n3. A typo: in line 11 of the abstract, 'Suggestion-aware Gradient Relative Policy Optimization (S-GRPO)'. Gradient -> Group.\n\nLIMITATIONS:\nYes. The authors have addressed the limitations in Appendix.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work constitutes a significant advance for reliable GUI automation. Its formalization of pre-operative critique, coupled with S-GRPO and high-quality datasets, resolves critical safety/efficiency challenges. The dual static/dynamic validation demonstrates both diagnostic precision and operational efficacy, offering a foundational framework for safe human-AI interaction.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n- The work introduces the first formalized *pre-operative critic* mechanism for GUI automation, marking a conceptual shift from post-hoc correction to proactive error prevention. It proposes S-GRPO, a novel suggestion-aware reward formulation that extends GRPO to support multimodal critique generation. Furthermore, the authors develop a reasoning-bootstrapping pipeline to collect high-quality data without ground-truth, effectively addressing the lack of existing GUI critic datasets.\n\n- The paper demonstrates strong experimental design, evaluating the approach on both static (GUI-Critic-Test) and dynamic (AndroidWorld) benchmarks across mobile and web platforms. Comprehensive ablation studies (Table 3–4, Fig. 3) provide clear justification for key design choices.\n\nWeakness:\n\nI don’t have major concerns, but there are a few minor points worth noting:\n\n- The method shows relatively weak performance on the GUI-W benchmark (63.08% vs. 69.20% on GUI-I), raising concerns about its cross-platform generalization, particularly in desktop or web-based environments. This aspect is not sufficiently discussed in the paper.\n\n- The experiments are primarily conducted on scripted benchmarks like AndroidWorld. It remains unclear how well the method handles more complex, real-world conditions—such as dynamic UIs, unpredictable layout changes, or network-induced delays—which are common in practical GUI automation tasks.\n\nQUESTIONS:\n- How is Isimilar(s,s′)*I*similar​(*s*,*s*′) computed in Equation (3)? The paper lacks detail here. Is the similarity judged by a vision-language model (e.g., Qwen2.5-VL-72B), or is it rule-based? More clarity would help understand how reliable and generalizable this reward is.\n\n- The critic model appears to perform reasoning-like functions similar to those found in advanced GUI action models with built-in \"thinking\" capabilities. This raises an important question: does introducing a separate critic reduce the need for reasoning within the action model itself, or do they complement each other? Clarifying this relationship would provide deeper insight into the system design and its modularity.\n\n- The paper does not discuss the computational overhead introduced by the real-time pre-critic mechanism. Since the critic is designed to operate before the GUI action model, it may lead to increased latency in practical applications. A discussion on runtime efficiency and its trade-offs would help assess the feasibility of deployment in real-world systems.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles the problem of catching step-level errors before they cripple an LLM-driven GUI agent. It introduces a pre-operative critic architecture embodied in GUI-Critic-R1, a vision-language model fine-tuned to judge a candidate action, explain why it might back-fire and suggest a fix and all before the click lands.\n\nAt the heart of the system is Suggestion-aware Group Relative Policy Optimization (S-GRPO). After a lightweight RFT *cold-start*, S-GRPO samples groups of candidate critiques; a tri-part reward (format, accuracy, and a novel suggestion reward that enforces actionable fixes) ranks them, and policy gradients push the model toward the best-scoring alternative. Algorithm 1 (S-GRPO) orchestrates this loop, while a built-in KL term keeps the critic tethered to a reference policy for stability.\n\nTraining data come from a reasoning-bootstrapping pipeline: positive actions are harvested from existing GUI trajectories; negative actions are hallucinated by an off-the-shelf agent and filtered by GPT-4o; Chain-of-Thought critiques are then boot-strapped in a progressive <thinking/score/suggestion> format to fill the gaps. The result is GUI-Critic-Train (~11k samples, 6k with full CoT) and GUI-Critic-Test spanning instruction, scenario and domain generalisation settings (GUI-I/S/W).\n\nWith a 7B Qwen-VL backbone, GUI-Critic-R1 lifts static critic accuracy on GUI-I from 54.9% to 69.2%, and suggestion accuracy from 43.1% to 52.4%, outperforming GPT-4o and other open models. Dropped into AndroidWorld as a plug-and-play pre-critic, it pushes the downstream task success rate from 22.4% to 27.6% while shortening action traces, beating both post-hoc reflection and larger pretrained critics.\n\nOverall, the work shows that coupling boot-strapped GUI reasoning data with S-GRPO yields a compact yet reliable critic that lets GUI agents look before they leap, preventing irreversible clicks and trimming redundant detours in real-time automation.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n**Critical Safety Angle:** Tackles the overlooked problem of *pre-emptively* vetoing bad GUI actions, turning reactive agents into agents that can “look before they leap.”\n\n**Novel Pre-Critic Architecture:** Couples a vision-language backbone with explicit <thinking/score/suggestion> reasoning slots, so the critic not only flags errors but explains and repairs them in natural language.\n\n**S-GRPO Innovation:** Introduces Suggestion-aware Group Relative Policy Optimization - an RL recipe that adds a *suggestion reward* on top of format and accuracy terms, directly optimising for actionable fixes rather than mere classification. \n\n**Boot-Strapped Dataset Contribution:** Releases GUI-Critic-Train/Test, built with a reasoning-bootstrapping pipeline that mixes real trajectories, hallucinated negatives, and GPT-4o-verified critiques.\n\n**Good Empirical Gains:** Lifts static critic accuracy on GUI-I from 54.9% to 69.2% and downstream AndroidWorld task success from 22.4% to 27.6%, outperforming GPT-4o and larger open models.\n\n**Lightweight Footprint:** Achieves those gains with a 7B model, showing that safety and efficiency can co-exist without resorting to larger models.\n\n**Plug-and-Play Integration:** Can be inserted as a decision gate in existing agents with no retraining of the actor, immediately shortening action traces and avoiding irreversible clicks.\n\n---\n## Weaknesses\n**Latency Overhead:** Every action now triggers a VLM forward pass, adding perceptible delay; the paper reports gains but no detailed timing budget for real-time RPA scenarios.\n\n**Limited Ablations:** The ablation table omits variants without the suggestion reward or with alternative optimisers, leaving open how much each component truly matters. \n\n**False-Positive Risk:** A conservative critic occasionally blocks valid but unfamiliar actions, lowering overall task throughput and yet false-negative/false-positive trade-offs are not quantified.\n\nQUESTIONS:\n1. Exactly how much end-to-end delay does the critic add per GUI action (mean, p95, worst-case) and how does it scale with screen resolution?\n2. How much does each S-GRPO reward head (format, accuracy, suggestion) contribute, and how sensitive are results to group size k?\n3. At the chosen threshold, how often does the critic wrongly block a valid action or permit a harmful one?\n4. Does a critic trained on AndroidWorld transfer to desktop or web GUIs?\n5. Will GUI-Critic-Train/Test and training scripts be released (licence + timeline)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a critic model, GUI-Critic-R1, which can detect errors and provide corrective suggestions before actually executing actions in GUI tasks. To train this model, the authors first construct a dataset consisting of successful trajectories and sampled incorrect operations. Based on this dataset, critiques are collected via rejective sampling. GUI-Critic-R1 is trained with 1 epoch of SFT, followed by 10 epochs of GRPO. As the dataset contains two supervised signals, correctness and suggestion, the paper introduces an additional suggestion reward into GRPO, alongside the accuracy and format rewards. Experiments demonstrate that GUI-Critic-R1 outperforms its base model, Qwen-7B, and is comparable to some closed-source models.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n* This paper presents a complete pipeline, covering both data collection and model training.\n* This paper introduces a novel perspective by making the critic model to provide not only a correctness signal but also suggestions.\n\n**Weakness**\n* The idea of a pre-operative critic is interesting, but its necessity is not fully convincing. Since the dataset already contains step-level correctness labels and suggestions, it is natural to ask why not use this data to directly train a better policy model, instead of training a separate critic. Adding a critic makes the system more complex, and it is not clear if the extra component brings more benefit than directly improving the policy. Also, in some dynamic or partially observable GUI environments, it might be hard for the critic to predict the outcome of an action without actually running it.\n* The overall technical novelty is somewhat limited. This paper mostly adapts existing RFT + RL techniques to a specific GUI automation context, with relatively limited insights for broader domains.\n* The data collection process relies on successful trajectories from public datasets and filtering with GPT-4o, which limits the scalability of the approach. Step-level correctness signals and suggestions may become unavailable when GUI tasks extend beyond the coverage of existing datasets or exceed the reasoning capabilities of GPT-4o.\n\nQUESTIONS:\n* In Section 3.3.1, the RFT stage involves two datasets: one with CoT and one without. Are different system prompts used for these two datasets to indicate whether the response should include a CoT?\n* After the critic model outputs the correctness score, critique, and suggestion, how exactly does the agent utilize this information? If an action is judged to be incorrect, does the agent directly follow the suggested correction, or does it treat the critique and suggestion as context and re-generate a new action?\n* Typo in Table 4: the position of $r_s$ seems to be incorrect.\n\nLIMITATIONS:\nThe data collection process relies on successful trajectories from public datasets and filtering with GPT-4o, which limits the scalability of the approach. Step-level correctness signals and suggestions may become unavailable when GUI tasks extend beyond the coverage of existing datasets or exceed the reasoning capabilities of GPT-4o.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-07"}