Title: GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents
Venue: NeurIPS 2025
Accepted: yes
Keywords: R1-Zero-Like Training

GUI Agents

Grounding
Abstract: Recent Graphical User Interface (GUI) agents replicate the R1-Zero paradigm, coupling online Reinforcement Learning (RL) with explicit chain-of-thought reasoning prior to object grounding and thereby achieving substantial performance gains. In this paper, we first conduct extensive analysis experiments of three key components of that training pipeline: input design, output evaluation, and policy update—each revealing distinct challenges arising from blindly applying general-purpose RL without adapting to GUI grounding tasks. Input design: Current templates encourage the model to generate chain-of-thought reasoning, but longer chains unexpectedly lead to worse grounding performance. Output evaluation: Reward functions based on hit signals or box area allow models to exploit box size, leading to reward hacking and poor localization quality. Policy update: Online RL tends to overfit easy examples due to biases in length and sample difficulty, leading to under-optimization on harder cases. To address these issues, we propose three targeted solutions. First, we adopt a $\textbf{Fast Thinking Template}$ that encourages direct answer generation, reducing excessive reasoning during training. Second, we incorporate a box size constraint into the reward function to mitigate reward hacking. Third, we revise the RL objective by adjusting length normalization and adding a difficulty-aware scaling factor, enabling better optimization on hard samples. Our $\textbf{GUI-G1-3B}$, trained on 17K public samples with Qwen2.5-VL-3B-Instruct, achieves $\textbf{90.3\%}$ accuracy on ScreenSpot and $\textbf{37.1\%}$ on ScreenSpot-Pro. This surpasses all prior models of similar size and even outperforms the larger UI-TARS-7B, establishing a new state-of-the-art in GUI agent grounding.

GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents

Abstract

Recent Graphical User Interface (GUI) agents replicate the R1-Zero paradigm, coupling online Reinforcement Learning (RL) with explicit chain-of-thought reasoning prior to object grounding and thereby achieving substantial performance gains. In this paper, we first conduct extensive analysis experiments of three key components of that training pipeline: input design, output evaluation, and policy update-each revealing distinct challenges arising from blindly applying generalpurpose RL without adapting to GUI grounding tasks. Input design: Current templates encourage the model to generate chain-of-thought reasoning, but longer chains unexpectedly lead to worse grounding performance. Output evaluation: Reward functions based on hit signals or box area allow models to exploit box size, leading to reward hacking and poor localization quality. Policy update: Online RL tends to overfit easy examples due to biases in length and sample difficulty, leading to under-optimization on harder cases. To address these issues, we propose three targeted solutions. First, we adopt a Fast Thinking Template that encourages direct answer generation, reducing excessive reasoning during training. Second, we incorporate a box size constraint into the reward function to mitigate reward hacking. Third, we revise the RL objective by adjusting length normalization and adding a difficulty-aware scaling factor, enabling better optimization on hard samples. Our GUI-G1-3B , trained on 17K public samples with Qwen2.5-VL-3BInstruct, achieves 90.3% accuracy on ScreenSpot and 37.1% on ScreenSpot-Pro. This surpasses all prior models of similar size and even outperforms the larger UI-TARS-7B, establishing a new state-of-the-art in GUI agent grounding. The project repository is available at https://github.com/Yuqi-Zhou/GUI-G1 .

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1 Introduction

DeepSeek-R1-Zero [12] revolutionizes the post-training pipeline of Large Language Models (LLMs) by introducing the R1-Zero paradigm, which applies RL directly to base LLMs without relying on supervised fine-tuning (SFT) as an intermediate step. Motivated by this approach, recent work in the domain of GUI agents [25, 28, 40] has increasingly adopted RL, particularly the GRPO algorithm [34], in order to address two key limitations: (1) SFT requires large-scale, high-quality labeled datasets, resulting in significant computational costs; (2) existing open-source GUI agents trained with SFT often exhibit poor generalization to out-of-domain (OOD) scenarios [5, 27].

While RL has emerged as a popular choice for training GUI agents in recent work, attributing performance gains solely to the algorithm itself remains nontrivial. These R1-style models often differ in multiple dimensions-including backbone architectures, data sources, and training protocols-making it difficult to isolate the specific contribution of online RL. To better isolate the role of RL, we focus exclusively on the grounding task [11, 23], which we consider the core capability for effective GUI interaction [24]. Building on these observations, this work rethinks the role of RL in R1-style GUI agents training by (1) disentangling its algorithmic contributions from other system-level factors, and (2) focusing exclusively on grounding as the reinforcement objective.

For this, we decompose the R1-Zero-like training pipeline into three core components: input design, output evaluation, and policy update. Each reveals distinct challenges arising from blindly applying general-purpose RL without adapting to grounding tasks. First , we observe that the grounding performance of the state-of-the-art R1-style model, InfiGUI-R1 [25], drops as reasoning increases in Sec. 3.1, suggesting that reasoning templates may not benefit grounding in GUI agents. Second , we find that commonly used reward functions based on hit signals or box area lead to opposite forms of reward hacking in Sec. 3.2: the former encourages smaller boxes with higher accuracy, while the latter favors larger boxes to increase Intersection over Union (IoU). Finally , we identify two biases in the GRPO objective: length bias [26] and difficulty bias in Sec. 3.3. Length bias encourages longer but incorrect responses, which, as previously observed, further degrade grounding performance. Difficulty bias treats all samples equally, hindering the model's ability to learn from more challenging examples. Together, these biases make it harder for the model to learn from difficult samples.

To address the above issues, we implement the following improvements. First, we introduce the Fast Thinking Template , which encourages the policy to generate answers directly during training. Second, to counteract the hacking in common reward functions that prefer boxes of different sizes during policy optimization, we propose a box-size-based reward function as a constraint. Finally, we remove the length normalization term from the original GRPO objective, following [26]. We also introduce a difficulty coefficient for each sample's loss, allowing the model to receive greater gradients for more challenging samples. The difficulty coefficient is calculated from the relative box size, which serves as a proxy difficulty indicator in the grounding task [18].

Building on the above solutions, we train our model, GUI-G1-3B , using Qwen2.5-VL-3B-Instruct and a small (about 17K) set of grounding samples, showing strong performance with limited supervision from public datasets such as UI-BERT [3] and OS-Atlas [39]. Our model achieves new state-of-the-art performance on GUI grounding benchmarks, with 90.3% accuracy on ScreenSpot [8] and 37.1% on ScreenSpot-Pro [18]. It surpasses the previous best R1-style GUI agent, InfiGUI-R1 [25], while requiring significantly less data, fewer output tokens, and fewer training stages.

In summary, the contributions of this paper are as follows: ( 1 ) We identify three distinct challenges in the R1-Zero-like training pipeline of R1-style GUI agents: grounding is harmed by longer reasoning due to grounding's reliance on image tokens; common reward functions induce size-sensitive reward hacking; and GRPO biases agents toward simpler examples due to its objective. ( 2 ) Wefurther analyze and propose three solutions: a Fast Thinking Template for policy training, a box size-based reward to regularize box size, and a modified GRPO with difficulty weighting and no length normalization. ( 3 ) Trained on only 17K fully open-source grounding samples, our GUI-G1-3B achieves state-of-the-art performance while using fewer tokens when testing.

2 R1-Zero-Like Training Paradigm for GUI Grounding

We begin by describing how to train Multimodal Large Language Models (MLLMs) for grounding tasks. Given a screenshot s and a textual description d , the MLLM is trained to predict the target

location B , typically represented as a bounding box or a point. Following prior work [8], we formulate grounding as a language generation task, where the MLLM produces a response o that includes the predicted location as well as additional components such as the reasoning process or objective descriptions in Figure 1. In our implementation, the predicted location is expressed as a bounding box B pred = (ˆ x 1 , ˆ y 1 , ˆ x 2 , ˆ y 2 ) , where x and y denote the horizontal and vertical coordinates, respectively. This prediction is evaluated against the ground-truth box B gt = ( x 1 , y 1 , x 2 , y 2 ) .

When RL is applied via the algorithm like GRPO [34], a template is first used to guide the response format, and the model generates N candidate responses O = { o 1 , o 2 , . . . , o N } . Each response is then evaluated using a set of rule-based reward functions, yielding a reward set { r 1 , r 2 , . . . , r N } . The relative advantage A i of each response is computed as:

where mean and std denote the mean and standard deviation of the rewards, respectively. Finally, the policy model is optimized using the GRPO objective with KL-divergence regularization.

3 How R1-Zero-Like Training Affects Grounding for GUI Agents?

We first aim to understand R1-Zero-like training paradigm for grounding task in GUI agents by examining three essential components: the input design (template) (Sec. 3.1), the output evaluation (reward function) (Sec. 3.2), and policy update (RL objective) (Sec. 3.3). Finally, we present our model, GUI-G1 , in Sec. 3.4, where we also summarize and compare our approach with existing R1-style agents to demonstrate its advantages in grounding tasks.

3.1 Analysis on Template

Recent R1-style GUI agents have increasingly incorporated explicit reasoning by prompting the model to 'think before action' [25, 28, 40], as illustrated in Figure 1. For example, InfiGUI-R1 [25] uses a Slow Thinking Template . While such reasoning-augmented agents achieve strong performance, it remains unclear whether the gains truly arise from the reasoning process itself. In fact, we find that reasoning is often unnecessary for the grounding task in GUI agents. Before conducting the analysis, we formalize the model's input and output to ensure consistency across experiments. The input consists of an image segment s and an instruction component t ins, which includes the user query and a guiding template. The output o comprises the reasoning trace t think and the final response t ans, where t ans contains the predicted location B pred. We define the number of reasoning tokens n think as output tokens , and the tokens derived from the image n img as image tokens . The text ratio is given by n ins + n think n img + n ins + n think , where n ins is the instruction tokens number.

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Longer thinking leads to worse grounding performance. While prior work, such as OpenAIo1 [30] and DeepSeek-R1 [12] demonstrates that longer reasoning chains can enhance performance on System-2 tasks like mathematics and programming, more recent studies [19, 44] have found that introducing intermediate reasoning steps may impair performance in tasks such as image classification and GUI grounding. Building on these observations, we further find that longer reasoning chains

consistently degrade grounding accuracy on the ScreenSpot dataset [8], as shown in Figure 2(Left). This suggests that longer chains are not only unnecessary but can be increasingly detrimental in GUI grounding, especially when the target item to be grounded is text.

Grounding benefits from appropriately scaled image tokens rather than from scaled text thinking. In Figure 2 (Left Middle), we observe that the grounding performance of InfiGUI-R1-3B improves as the number of image tokens increases. This observation raises a central question: Is grounding more reliant on image tokens or text tokens? To investigate this, we first partition the test samples based on the number of image tokens, ensuring each subset has a comparable level of visual input. Within each subset, we further divide samples into two categories according to their text ratio and evaluate grounding accuracy for each. As shown in Figure 2 (Right), a higher text ratio consistently correlates with lower grounding performance, indicating that enriching visual content is more effective than injecting additional textual reasoning.

As shown in Figure 2, explicit reasoning does not always improve grounding accuracy. Inspired by Thinking, Fast and Slow [15], we propose a Fast Thinking Template that guides the model to generate grounding outputs directly without reasoning steps. As detailed in Appendix B, models trained with this template outperform baselines on ScreenSpot and ScreenSpot-Pro.

Template 1 ( Fast Thinking Template ) You are a helpful assistant. \nUser: Grounding instruction is: {question} Please help to locate and output the bbox coordinates in JSON format.\nAssistant:

Template 2 ( Slow Thinking Template ) You FIRST think about the reasoning process as an internal monologue and then provide the final answer.\nThe reasoning process MUST BE enclosed within <think> </think> tags. \n User: The screen's resolution is {width}x{height}. \nPoint to the UI element most relevant to '{question}', output its coordinates using JSON format:\n ''json\n[\n{{'point_2d': [x, y], 'label': 'object name/description'}}\n]''\nAssistant:

3.2 Analysis on Reward Function

The rule-based reward function introduced in DeepSeek-R1 [12] exemplifies a simple yet effective approach based on exact match. In grounding tasks, current reward functions for R1-style GUI agents are mainly categorized into Hit-based rewards [25, 28, 40] and IoU-based rewards [25] in Table 1. Here, ( x p , y p ) is the center of the predicted box, computed as x p = (ˆ x 1 + ˆ x 2 ) / 2 , y p = (ˆ y 1 + ˆ y 2 ) / 2 . The Hit-based reward checks whether predicted box center hits within B gt, while the IoU-based reward measures the IoU between B pred and B gt. While prior work has employed R Hit and R IoU as reward signals for grounding-based RL, it remains unclear how these objectives jointly influence training dynamics. To answer this, we implement both types of reward functions for a comparative analysis. The detailed experimental settings and evaluation metrics can be found in Appendix C. Unless otherwise specified, all subsequent analyses follow the same setup.

Individually optimizing R Hit and R IoU leads to conflicting reward hacking behaviors. As shown in Figure 3 (Left), optimizing R Hit improves accuracy but causes IoU to drop in later training. Conversely, optimizing R IoU enhances overlap quality but reduces accuracy. This illustrates reward hacking in GUI grounding, where models overfit to one objective at the cost of others. These metrics capture complementary yet competing aspects: R Hit focuses on correctly identifying the target box, while R IoU measures overlap with ground truth. Their conflict when optimized separately highlights the challenge of designing balanced rewards.

GRPO's sample selection bias toward different box sizes leads to reward hacking. To investigate the cause of reward hacking, we visualize two cases with predicted bounding boxes in Figure 4 (Left). Models trained with R Hit tend to produce boxes smaller than the ground truth, while R IoU leads to

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significantly larger boxes. This pattern is quantitatively confirmed in Figure 3 (Right), where the relative size of predicted boxes increases over training under R IoU, but decreases under R Hit. Further, as illustrated in Figure 4 (Right), the cause of these opposite trends lies in how GRPO's sample selection interacts with the reward functions: optimizing R Hit encourages the model to pick smaller boxes that better capture the core target region, improving accuracy, whereas optimizing R IoU favors larger boxes that yield higher overlap with ground truth, thus boosting IoU.

R Box helps mitigate reward hacking by regularizing box size. To address reward hacking, a straightforward solution is to jointly optimize both R Hit and R IoU. However, as shown in Figure 3, training may still be dominated by one of the two, resulting in suboptimal balance. To alleviate this, we introduce a new reward function R Box in Table 1. Here, x p 1 = 1 1 -| ˆ x 1 -x 1 | / image width , with similar definitions for the other terms. This reward encourages the predicted bounding box to match the ground truth in terms of size. As shown in Figure 3, incorporating R Box leads to further improvements in both accuracy and IoU, with predicted box sizes becoming more aligned with the ground truth. We also experiment with using R Box alone, but the model fails to produce outputs in the correct format. We hypothesize this is because R Box assigns non-zero rewards even to poorly grounded predictions, encouraging optimization on uninformative samples. Therefore, R Box should be used in conjunction with R Hit and R IoU, which directly reflect the evaluation metrics and serve as auxiliary constraints.

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𝑹𝐇𝐢𝐭 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝

𝑹𝑰𝒐𝑼 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝

Optimized Box

3.3 Analysis on GRPO Objective

Recent approaches to improving GUI agents [25, 28, 40] have adopted RL techniques, such as the GRPO algorithm proposed by DeepSeekMath [34]. GRPO optimizes the policy π θ by sampling a set of candidate responses { o i } N i =1 from the old policy π θ old for each input query q , where each response o i has length | o i | . The policy is updated based on a normalized advantage ˆ A i,t computed for each token, forming the objective J GRPO ( π θ ) :

where t is the index of the token in the response, ϵ is a hyperparameter that controls the maximum allowed deviation from the old policy, and clip ( · , 1 -ϵ, 1 + ϵ ) applies clipping to stabilize training.

In the setting of GUI grounding tasks, Eq. 2 introduces two biases (see also in Figure 5):

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Response-level length bias [26] : It has been observed [26] that GRPO introduces a length bias: longer responses are preferred among incorrect ones, while shorter responses are favored among correct ones. This arises from dividing the objective J GRPO ( π θ ) by | o i | , which amplifies the per-token gradient for shorter responses when the advantage is positive ( ˆ A i,t > 0 ), pushing the policy toward simpler correct outputs. Conversely, it encourages unnecessarily long incorrect answers. As shown in Figure 6 (Left), training gradually results in longer incorrect and shorter correct responses. This trend further harms performance, as longer outputs are shown to degrade accuracy in Section 3.1. Therefore, length bias in grounding tasks is especially problematic: it not only increases token count but also reduces overall quality.

Question-level difficulty bias : It has been noted in [26] that dividing the centered outcome rewards by std ( r 1 , r 2 , . . . , r N ) can lead the model to focus disproportionately on either harder or easier samples. However, we argue that assigning higher weights to harder samples during policy updates is desirable. In grounding tasks, the relative box size of the target can serve as a proxy for task difficulty [18]. Based on this intuition, we modify the original objective to w q · J GRPO ( π θ ) , where w q reflects the difficulty of query q . The weight w q is computed based on the relative box size, where a larger relative size indicates an easier grounding instance. Detailed computation is provided in Appendix C.3. Multiplying the objective by w q assigns greater gradients to harder samples, thus encouraging the model to focus on more challenging instances. In fact, length bias can also be viewed as a form of difficulty bias, as it guides the model toward generating longer incorrect responses, which exacerbates the difficulty of learning from such examples and indirectly shifts the focus toward easier samples.

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Experimental Results. We implement both improvements, with the results reported in Table 2 and in Table 9 (Appendix D.2). Mitigating length and difficulty biases consistently enhances model performance. Figure 6 (middle and right) further tracks the ratio of extreme samples, where all sampled responses are either correct or incorrect, throughout training. In the middle plot, our method initially lags on easy samples due to their lower weights, but gradually outperforms the original GRPO as these examples are eventually learned. In the right plot, our method maintains a lower proportion of extreme cases on hard samples, indicating that difficulty re-weighting facilitates better learning from challenging instances.

3.4 GUI-G1: A Tailored RL Visual Grounding Model

Based on the above analysis, we identify key limitations in existing training paradigms for grounding tasks. We now summarize our proposed improvements and present a comparison with prior methods.

Our method, GUI-G1, addresses the identified issues through:

Thinking leads to poorer grounding performance in Sec. 3.1 : We adopt a template without intermediate reasoning to prevent the policy from generating long thinking during training.

R Hit and R IoU cause opposing types of reward hacking in Sec. 3.2 : We combine R Hit and R IoU as the reward signal and introduce an additional R Box term to regularize predicted box sizes, mitigating reward hacking caused by box size mismatch.

Original GRPO introduces length and difficulty biases in Sec. 3.3 : We remove these biases by replacing | o i | with a constant Max_Tokens [26] and by weighting the GRPO objective J GRPO ( π θ ) with a difficulty coefficient w p .

To make the distinctions clearer, Table 3 provides a structured comparison between GUI-G1 and existing R1-style GUI agents in grounding tasks.

4 Experiments

In this section, we introduce the experimental setup for training and evaluating our proposed GUIG1-3B agent. We outline the implementation details, describe the training dataset and evaluation benchmarks, and provide a detailed comparison with state-of-the-art methods.

Implementation Details. Our model is built upon the Qwen2.5-VL-3B-Instruct and trained using the VLM-R1 framework [35]. The reward function follows the form R Hit + αR IoU + βR Box, where α is set to 0.25 and β to 0.125. We conduct training on 4 NVIDIA H800 GPUs over 3 days, with a global batch size of 32 and a learning rate of 1 × 10 -6 . No KL divergence regularization is applied. Only one training epoch is required.

Training Dataset and Evaluation Benchmarks. We construct a 17K-sample grounding dataset spanning three domains: Mobile (from UI-BERT [3]), Web (from OS-Atlas [39]), and Desktop (from OS-Atlas, covering Windows, Linux, and MacOS). More details of the training dataset are provided in the Appendix D.1. To ensure data quality, each sample is prompted eight times using Qwen2.5-VL-3B-Instruct, and those with consistently correct or incorrect responses are discarded [6]. For evaluation, we adopt ScreenSpot [8] and ScreenSpot-Pro [18]. While ScreenSpot assesses grounding performance across diverse platforms, including Mobile, Web, and Desktop, ScreenSpotPro emphasizes more challenging desktop scenarios, featuring high-resolution screens.

Performance Comparison on ScreenSpot. We compare GUI-G1-3B with a range of state-of-the-art open-source and proprietary GUI agents, using results reported in their original papers. Table 4 summarizes performance on the ScreenSpot benchmark. GUI-G1-3B achieves state-of-the-art results, outperforming proprietary systems like Gemini 2.0 [9], general-purpose models such as the Qwen2.5 series [4], GUI-specific SFT models like OS-Atlas [39] and UGround [11], as well as R1-style models including UI-R1 [28], GUI-R1 [40], and InfiGUI-R1 [25]. It also surpasses larger models like OS-Atlas-7B [39]. Despite its strong performance, our model is trained on only 17K samples and requires no intermediate reasoning steps. Moreover, it achieves higher inference efficiency by generating significantly fewer tokens than other methods (see Appendix D.3, Table 10).

Performance Comparison on ScreenSpot-Pro. As shown in Table 5, GUI-G1-3B achieves competitive performance on the challenging ScreenSpot-Pro benchmark, with an overall average score of

37.1%. It outperforms the larger UI-TARS-7B model (35.7%) and significantly surpasses the bestperforming R1-based model, InfiGUI-R1-3B (35.7%). Although both GUI-G1-3B and OS-Atlas-7B use the same training dataset, our model performs worse on the OS subset (16.1% vs. OS-Atlas-7B's 16.8%), suggesting that its gains mainly result from post-training that activates pretrained knowledge rather than from task-specific data. This demonstrates the robustness and generalization ability of our approach in real-world scenarios. Additionally, we apply our improved training approach to the GUI-G1 model across various model sizes and families, and reproduce the UI-R1 setting in the Appendix D.4. The results confirm that GUI-G1 maintains strong performance, highlighting the robustness and effectiveness of our method.

5 Related Work

Grounding for GUI Agents. Grounding is central to GUI agents research [38, 47], driving advances in data collection and model design. Early works such as VUT [21] and Spotlight [17] aligned task structures and modalities (e.g., screenshots, instructions) using BERT-based [10] representations, while RUIG [45] employed reinforcement learning to map instructions to UI coordinates. With MLLMs, the focus shifted to fine-tuning pretrained models for cross-platform interaction and GUI adaptation. ShowUI [23] improved efficiency by reducing redundant visual tokens, and FerretUI 2 [22] enhanced understanding via high-resolution encoding and cross-platform generalization. Aria-UI [43] introduced multi-turn grounding with sequential reasoning for dynamic multi-step interactions. More recently, OS-Atlas [39] and UGround [11] advanced the field by releasing large open-source datasets and training models robust to out-of-distribution tasks. Unlike these dataintensive, supervised approaches, our work explores how minimal data and an R1-Zero-like method can unlock MLLM grounding capability for GUI tasks.

R1-Zero-like Training for MLLMs. DeepSeek-R1-Zero [12] introduced a GRPO-based posttraining framework that enhances reasoning via structured outputs. This approach was extended to multimodal settings by Vision-R1 [14], MM-EUREKA [29], and VisualThinker-R1-Zero [46], all improving vision-language reasoning. LMM-R1 [32] achieved strong results with a two-stage RL scheme and low cost. However, recent work [19] found that reasoning-averse models can outperform reasoning-based ones in multimodal classification, suggesting reasoning is not always beneficial. In GUI agents, studies such as UI-R1 [28], GUI-R1 [40], and InfiGUI-R1 [25] verified the effectiveness of R1-Zero-like training in action prediction and grounding, showing consistent gains on ScreenSpot [8], ScreenSpot-Pro [18], and AndroidControl [20]. This work focuses on the grounding task and revisits whether the standard R1-Zero-like settings are suitable for GUI scenarios.

6 Conclusion

In this work, we revisit the R1-Zero-style training setup for GUI grounding agents from three aspects: input design, reward evaluation, and policy update. We show that lengthy reasoning impairs grounding performance and propose a Fast Thinking Template to address it. We then find and analyze opposing types of reward hacking issues in existing reward designs and introduce a box-size constraint to mitigate them, leading to improved performance. Finally, we examine GRPO's length and difficulty biases in grounding tasks and address them by removing length normalization and incorporating difficulty-based weighting. With only 17K samples, our GUI-G1-3B surpasses larger R1-style models on ScreenSpot and ScreenSpot-Pro.

7 Acknowledgments

This work was funded by the National Key R&D Program of China (2023YFA1008704), the National Natural Science Foundation of China (62472426,62376275). Supported by fund for building worldclass universities (disciplines) of Renmin University of China. Work partially done at Beijing Key Laboratory of Research on Large Models and Intelligent Governance, and Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE. We thank Yuchong Sun for helpful discussions and insights during the development of this work.

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A Limitations

While our method demonstrates strong performance in GUI grounding, there remain several limitations that offer directions for future work: ( 1 ) Focus on grounding. The current work focuses on grounding, which is essential for GUI agents, but does not cover tasks like action prediction or long-horizon planning. Future research can extend this approach to support full decision-making in GUI interaction. ( 2 ) Scope of RL analysis. The study mainly examines online reinforcement learning, especially GRPO. Other factors such as dataset composition, model design, and hyperparameter tuning are not fully explored and deserve further analysis. ( 3 ) Limited training data. Our model is trained on a relatively small set of public datasets, which constrains its performance ceiling. In future work, we plan to scale up training using larger and more diverse datasets, such as those adopted in GUI-R1 [40], to further improve generalization and robustness.

B Fast Thinking Template versus Slow Thinking Template

To examine the role of reasoning in grounding tasks, we formally define two instruction templates, the Fast Thinking Template and the Slow Thinking Template. We then train models under both settings to compare their effects on grounding performance. The detailed training configurations and implementation settings are provided in Section B.2.

B.1 Formal Definition of Fast and Slow Thinking

Following Thinking, Fast and Slow [15], Fast Thinking corresponds to System 1, characterized by intuitive, automatic, and effortless responses. Slow Thinking corresponds to System 2, characterized by deliberate, analytical, and effortful reasoning. In the context of large language models, Fast Thinking denotes direct answer generation without intermediate reasoning, as exemplified by the non-thinking mode in Qwen3 [42]. Slow Thinking refers to explicit multi-step reasoning processes, such as those elicited through chain-of-thought prompting. In our work, Fast Thinking represents direct grounding prediction from visual inputs, whereas Slow Thinking involves reasoning over the observed scene before producing the final grounding output.

B.2 Experimental Setting

We train our Qwen2.5-VL-3B-Instruct model on the UI-R1-3B-Train dataset 2 , which consists of 101 samples with grounding annotations. During training, the vision encoder is kept frozen. We set the learning rate to 1 × 10 -6 and the sampling temperature to 0.9, generating eight responses per prompt. Training is performed on four L20 (48G) GPUs with a batch size of four samples per GPU, β = 0 , and a gradient accumulation step of one.

B.3 Experimental Results

The results in Table 6 and Table 7 show that encouraging direct answers with Fast Thinking Template consistently leads to better performance, supporting our hypothesis that explicit reasoning can hinder grounding effectiveness.

C Analysis Experiments Settings

C.1 Training Details

We fine-tune all parameters of the Qwen2.5-VL-3B-Instruct model using samples evenly drawn from three domains: mobile (UIBERT [3]), web, and desktop (OS-Atlas [39]). Due to computational constraints, we randomly sample 300 grounding examples from each domain. Despite the relatively small dataset, the model achieves strong performance after fine-tuning, ensuring the reliability of our subsequent analysis. Training follows the default setup of the VLM-R1 repository [35], using 8 rollouts per example and no KL-divergence regularization by default.

C.2 Evaluation Metrics

Specifically, we use the relative box size to measure the size of the predicted bounding box. It is calculated as:

where IMAGE_WIDTH and IMAGE_HEIGHT denote the pixel width and height of the input image, respectively.

C.3 Difficulty-Aware Weighting Strategy

To compute the difficulty weight w q for each sample based on its relative box size λ q ∈ (0 , 1] , we first take the inverse of the size to reflect the intuition that smaller boxes are harder: λ ′ q = 1 λ i . We then normalize the inverted values to the range [0 , 1] by computing ˜ λ q = λ ′ q -min i λ ′ i max i λ ′ i -min i λ ′ i . Finally, we linearly rescale the normalized scores to the interval (0 . 5 , 1 . 5] to obtain the final difficulty weights: w q = 0 . 5 + ˜ λ q . Putting everything together, the final formula is

This ensures that harder samples (with smaller boxes) receive higher weights, while keeping the values in a stable and bounded range.

D More Experiments Details

D.1 Training Data Composition

To provide a comprehensive grounding resource across diverse platforms, we construct a dataset containing 17K samples distributed across three representative domains: Mobile, Web, and Desktop.

The Mobile domain is derived from the UI-BERT dataset [3], which consists of user interface data collected from Android applications.

The Web domain is sourced from OS-Atlas [39], including interactive web elements and browserbased environments.

The Desktop domain is also based on OS-Atlas, but focuses on native applications and interfaces from major desktop operating systems, including Windows, Linux, and MacOS.

Each domain contains a balanced set of grounding instances that pair natural language commands with corresponding UI elements. Table 8 summarizes the number of samples collected in each domain.

D.2 Debias Results on ScreenSpot-Pro

The results in Table 9 indicate that both length bias correction and difficulty reweighting enhance performance on ScreenSpot-Pro. The limited effect of length bias removal may stem from the predominance of high-resolution images, where text tokens contribute little relative to image tokens. In contrast, difficulty reweighting produces notable improvements, validating its benefit for harder samples.

D.3 Output Token Efficiency Analysis

To assess the efficiency of different models during inference, we compare the average number of output tokens generated per example across Mobile, Desktop, and Web domains on ScreenSpot [8]. As shown in Table 10, GUI-G1-3B generates substantially fewer tokens than InfiGUI-R1-3B [25] in all domains-approximately one-third as many on average-while maintaining or even improving task accuracy. This compact output not only reduces computational cost but also reflects the model's ability to produce precise and concise responses without relying on verbose intermediate reasoning.

D.4 Experiments Across Various Model Sizes and Families

To evaluate the generality of our method, we conduct experiments across different model families and sizes. We apply our approach to three models: InternVL3-2B-Instruct (a smaller model from a different family), Qwen2.5-VL-3B-Instruct, and Qwen2.5-VL-7B-Instruct (a larger variant). Following the training protocol of UI-R1 [28], we retrain the 7B and 2B models on the same dataset, while Qwen2.5-VL-3B-Instruct is retrained using our proposed method for direct comparison. Due to resource constraints, LoRA fine-tuning is applied to the 7B model, whereas the others are fully fine-tuned. All models use identical training data and hyperparameter settings for a fair comparison. Additional training details are provided in Appendix B.2.

The results in Table 11 and Table 12 show that our method consistently improve performance across both Qwen and InternVL families, although InternVL generally shows lower baselines. This trend aligns with recent observations that most leading GUI agents [28, 40, 25] adopt Qwen as their backbone. In terms of model scale, increasing from 3B to 7B unexpectedly results in lower grounding

accuracy. We attribute this to two factors: (a) the 7B model relies on LoRA instead of full fine-tuning, and (b) larger models tend to benefit less from task-specific grounding fine-tuning, as similarly observed in Table 1 of GUI-R1 [40]. For example, UI-TARS-72B performs worse on ScreenSpot (88.4) than the smaller UI-TARS-7B (89.5). Our results on the Qwen series further confirm that grounding performance does not necessarily scale with model size.

E Broader Impacts

Our work contributes to the development of more robust and accurate GUI agents by addressing key training challenges in reinforcement learning for visual grounding. This could improve the reliability of accessibility tools and human-computer interaction systems. However, care should be taken when deploying such agents in real-world systems, as reward design choices may cause unintended behavior such as reward hacking or bias toward easy cases. We encourage future research to further study fairness, robustness, and privacy considerations in GUI agent training and deployment.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies methods for training\tagents to navigate Graphical User Interfaces (GUIs) using R1-style post-training using RL. They\tinvestigate 3 different\tdesign components: the effect of input templates, effect and limitations of reward functions used for GUI environments, and the policy objective. They propose several improvements to these\tcomponents based on GUI\tenvironments:\n- whereas templates which encourage long chains\tof though (CoT)\thave proven effective in reasoning-based tasks (like math and coding), here they find that they\tare detrimental\tfor more vision-based tasks like localizing objects in a screenshot, and they find that removing intructions\tencouraging CoTs improves performance for object grounding in GUIs.\n- they find that existing reward functions for object grounding in GUIs, such as hit rate or IOU, lead to reward hacking behaviors. Based on this observation, they propose a different\treward function\twhich is not prone to these types of reward hacks.\n- they find that the GRPO objective produces two biases for GUI-based tasks: 1) biased lengths due to a term dividing by the output length 2) a bias based on difficulty due to the division by standard deviation of rewards. They propose two modifications, where the division by\toutput length is replaced by a constant\tand the\tobjective is weighted by a proxy for question difficulty, which\there is\tbased on the bounding box size.\n\nWith these 3 modifications, the system is trained on GUI-based benchmarks, where it leads to improvements over previous\tmethods.\n\n\nFirst, I should mention that GUI-based agents are not my research area, and I am not very familiar with the main methods, benchmarks and prior work other, hence my review and recommendation should be weighted accordingly. \nOverall, this paper seems very specific to GUI-based environments: the template, reward functions and modifications to the GRPO\tobjective are motivated by this class of problems and failure modes of previous\tmethods on them.\nWhile the improvements are reasonable, it's unclear to me that these insights would transfer to other domains or have broader applicability (the paper does not claim or present any evidence of this). The paper seems quite application-focused and I think it might be better suited to a venue more focused on this area, rather than NeurIPS which (at least historically) tends to be more\tfocused on general ML methods. For this reason, I'm recommending reject.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\n- The ablations and analyses appear carefully done, with the claims backed up by results and visualizations showing the described behavior and failure modes.\n- The empirical results\tfor the\tfinal system appear to improve noticeably over the baselines, and many different methods are included in the comparison.\n\n**Weaknesses**:\n\nAs mentioned above, the main weakness in my opinion is that the insights do not transfer much outside the area of GUI-based agents. They are quite specific to the modalities which the\tcurrent\tsystem uses (for example, bounding boxes over areas of the screen, reward functions based on\tbounding boxes and object grounding on the screen, etc). So, other than\tresearchers specifically working in this area, the paper may not be that relevant.\n\nQUESTIONS:\nThe inputs and output spaces described in lines 102-104 could be clearer. \n\n- For example, it wasn't clear to me what exactly the action space of the agent is: does it jointly produce text and a bounding box? are there separate heads for each? Is it all in tokens produced autoregressively? Are other keys part of the action space? \n\n- What exactly is the input - I understand it's an image, is it just RGB pixels? Or are there other post-processing steps applied.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper performs a systematic study on the impact of using R1-zero-like training for GUI agents along 3 axes: input design, output evaluation, and policy update. The authors note that the current R1-zero paradigm causes performance degradation for GUI agents. They propose solutions along each axis tailored to GUI agents and achieve state-of-the-art performance in the same size model family and a larger UI-TARS-7B on the ScreenSpot and ScreenSpot-Pro datasets. This paper provides useful insight for the GRPO training design of GUI agents.\n\nSTRENGTHS AND WEAKNESSES:\nThank you for sharing your work. Below are some strengths/weaknesses points. The main point that could be very useful to address is studying performance on different base models (please see the questions section). \n\nStrengths:\n-    Significance & Originality: This paper points to interesting insights regarding training GUI agents with GRPO. To the best of my knowledge, this is the first paper to do so. \n-    Clarity & quality: The paper is well organized and the writing is clear. Figures/tables are clear and helpful.\n\nWeaknesses:\n-    Clarity & quality:  \n      -    The insights highlighted in the paper are only evaluated on one base model. Thus, it is unclear whether the results found are model-specific or general. \n       -    It is generally important to evaluate RL methods over several runs due to high variance.\n\nQUESTIONS:\n1) Do the authors plan to release their checkpoint? This model could be very useful for the research community to use and build on top of. \n\n2) How does the proposed method perform when using different base model families? How does this method scale with model size?\n\n3) Would it be possible to perform multiple runs and report mean/std?\n\n4) Could there be any potential downstream effects on GUI control tasks when using the proposed training paradigm (e.g., degradation in downstream web navigation performance due to training to limit reasoning)?\n\nLIMITATIONS:\nYes to negative societal impact. \nNo to limitations regarding statistical significance testing and base model ablations (please see the questions section).", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper performs a comprehensive analysis of R1-style training for the GUI grounding objective. They identify three bottlenecks. 1. The GUI grounding performance actually decreases with increasing output tokens (thinking tokens). 2. Using only hit-based or IOU-based reward functions leads to reward hacking, with hit-based rewards favoring smaller boxes and IOU based reward functions favoring larger boxes. 3. The GRPO objective leads to overfitting easier to ground samples due to the generation of fewer output tokens. They see a trend where the examples that the model gets correct have increasingly smaller chains of thought and the examples the model gets wrong have increasingly longer chains of thought. \n\nThe authors propose GUI-G1 model to mitigate these three issues. This is done through the use of a Fast thinking template, which skips reasoning, a combination of IOU, hit-based, and additional box-based reward functions, and the use of a difficulty-aware objective, which favors the contribution of examples with smaller ground truth bounding boxes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \n1. This paper is well-written and timely. The authors clearly analyse and describe the problems encountered by current GUI grounding models and propose well-motivated solutions to these problems. \n2. The performed analysis is thorough and offers useful insights that can be used in future work for improving on GUI grounding models. \n3. The GUI-G1 model shows good positive results on both ScreenSpot and ScreenSpot-pro. \n4. Interestingly, the method requires less training data, and the resulting model gets better results in fewer output tokens compared to previous works. \n\nWeaknesses\n1. The section on the thinking template is a bit confusing. I have added some follow-up questions in the question suggestions. More importantly, an ablation exclusively on the thinking templates is missing. While the analysis does point to better results with fewer tokens, this analysis is performed on a different model (InfiGUI-R1). Also, the analysis only gives a hypothesis that needs to be validated through an explicit ablation in the GUI-G1 model. \n2. The net increase from the difficulty-aware addition to the objective seems very small in Table 2. I think this objective should be evaluated on Screenspot-pro to reflect its benefit.\n\nQUESTIONS:\nQuestions: \n1. In Figure 2, right, what are the exact specifications of Low and High Text ratios, and how do you get them? Is this an intervention experiment where low is with a fast thinking template and high is with a slow thinking template? Or is it just an observation where you threshold some value that defines how many is high and how many is low?\n2. From my understanding, higher image tokens are obtained with higher resolution tokens only. So, how do you get different text ratios for the same image size? Is this just by controlling the number of generated tokens? Is there some other way of enhancing the image and increasing the image tokens? \n\nSuggestions: \n1. Please include the UI-TARS-7B and UGround-v1-7B baselines in the GUI-specific models section of Table 4 for a better comparison, since you are comparing with other 7B models, and these models are compared in other tables.\n2. Line 198 should say Figure 6.\n3. The difficulty-aware objective should be validated on Screenspot-Pro.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper analyzes the R1-Zero-like training approach applied to GUI grounding tasks, identifying three key issues in current methods: reward hacking resulting from the reward function, biases inherent in the GRPO training objective, and degraded performance due to excessive reliance on lengthy textual reasoning. To mitigate this issue, the authors propose corresponding improvements, including (1) a new prompt designed to reduce unnecessary textual reasoning, (2) a constraint on reward function based on bounding box size to deal with reward hacking, (3) normalization of the RL objective with response length and sample difficulty.  Leveraging these improvements, the authors train a GUI grounding model based on Qwen2.5-VL-3B-Instruct, achieving strong results on the ScreenSpot and ScreenSpot-Pro benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: \n1. The paper provides helpful empirical insights into the limitations of current GUI grounding training pipelines, such as reward hacking and biases in the RL objective. These insights contribute meaningfully to improving model training. \n\n2. The proposed solutions are effective and well-motivated, addressing the identified limitations. Experimental results validate that the proposed methods lead to good performance on two widely adopted GUI visual grounding benchmarks, ScreenSpot and ScreenSpot-Pro. \n\nWeakness:\n1. The performance of the baseline model (Qwen2.5-VL-Instruct-3B) is omitted (marked \"--\") in Tables 4 and 5 without explanation. This omission raises questions about the comparability and validity of the reported improvements. The authors should clarify why these baseline results are absent, such as indicating if the base model struggled significantly with instruction-following tasks.\n\n2. The writing and presentation could benefit from some improvements. I have listed some uncertainties in the question section in detail.\n\nQUESTIONS:\n1. Lines 91-92 appear to use \"output evaluation\" and \"reward function\" together. It would be great if the authors could further elaborate the difference between these two terms. Otherwise, standardizing the terminology would enhance clarity.\n\n2. The concepts of \"Slow Thinking\" and \"Fast Thinking\" are under-specified and somewhat ambiguous. It would be great if the authors could provide a formal definition of these two concepts. \n\n3. In line 83, the term \"template\" is unclear. Could the authors explicitly clarify what a \"template\" entails in this context? Is it simply a specific part of the prompt describing the output format, or does it encompass additional instructions for the model's reasoning process?\n\nLIMITATIONS:\nNo major limitation", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-06"}