Title: SE-GUI: Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: gui agent; reinforcement learning; visual grounding
Abstract: Graphical User Interface (GUI) agents have made substantial strides in understanding and executing user instructions across diverse platforms. Yet, grounding these instructions to precise interface elements remains challenging—especially in complex, high-resolution, professional environments. Traditional supervised fine-tuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce a reinforcement learning (RL)-based framework that incorporates three core strategies: (1) seed data curation to ensure high-quality training samples, (2) a dense policy gradient that provides continuous feedback based on prediction accuracy, and (3) a self-evolutionary reinforcement finetuning mechanism that iteratively refines the model using attention maps. With only 3k training samples, our 7B-parameter model achieves state-of-the-art results among similarly sized models on three grounding benchmarks. Notably, it attains 47.3\% accuracy on the ScreenSpot-Pro dataset—outperforming much larger models, such as UI-TARS-72B, by a margin of 24.2\%. These findings underscore the effectiveness of RL-based approaches in enhancing GUI agent performance, particularly in high-resolution, complex environments.

SE-GUI: Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning

Abstract

Graphical User Interface (GUI) agents have made substantial strides in understanding and executing user instructions across diverse platforms. Yet, grounding these instructions to precise interface elements remains challenging-especially in complex, high-resolution, professional environments. Traditional supervised fine-tuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce a reinforcement learning (RL)-based framework that incorporates three core strategies: (1) seed data curation to ensure high-quality training samples, (2) a dense policy gradient that provides continuous feedback based on prediction accuracy, and (3) a self-evolutionary reinforcement finetuning mechanism that iteratively refines the model using attention maps. With only 3k training samples, our 7B-parameter model achieves state-of-the-art results among similarly sized models on three grounding benchmarks. Notably, it attains 47.3% accuracy on the ScreenSpot-Pro dataset-outperforming much larger models, such as UI-TARS-72B, by a margin of 24.2%. These findings underscore the effectiveness of RL-based approaches in enhancing GUI agent performance, particularly in high-resolution, complex environments.

1 Introduction

Graphical User Interface (GUI) agents have become increasingly capable of executing user commands across diverse platforms [1; 2]. Yet, a core challenge remains: accurately grounding natural language instructions to the correct elements on the interface [3; 4; 5]. While Supervised Fine-Tuning (SFT) has been proven to be effective in simple scenarios [6; 7; 8], it faces two major limitations: requiring large volumes of diverse data and exhibiting weak generalization in complex and high-resolution professional settings [9; 10].

To address these issues, reinforcement learning (RL) offers a promising alternative. Unlike SFT, RL enables models to learn from structured and incremental feedback via reward functions, guiding them toward more precise grounding. Among RL-based methods [11; 12; 13], Group Relative Policy Optimization (GRPO) [13] stands out for its efficiency. It replaces heavy value models with a simple

45

35

30

10

ScreenSpot-Pro Acc (%)

5

15

20

25

40

ShowUI-2B

UI-TARS-2B

Qwen2.5-VL-3B

OS-Atlas-7B

Qwen2.5-VL-7B

Uground-V1-

7B

UI-TARS-7B

Qwen2.5-VL-32B

UI-TARS-72B

SE-GUI-7B

18B

CogAgent-18B

SE-GUI-3B

Qwen2-VL-72B

50

Closed Source

SS-Web

SS-Mobile

SS-Desktop

SSv2-Web

SSv2-Mobile

SSv2-Desktop

OMiniACT

AC-Low

AC-High

SE-GUI

Uground-V1

Grounding

Agent

75.7

72.5

74.5

68.2

86.5

91.4

34.4

32.8

62.6

85.6

48.4

52.8

87.0

78.7

95.2

74.9

87.1

75.1

rule-based reward system, reducing the computational burden. However, typical approaches [9; 10] rely on binary (0/1) rewards, which are often sparse. Such sparsity impedes early-stage training, as incorrect predictions commonly receive identical zero rewards, resulting in uniform advantage estimates and limited gradient information for effective optimization.

Furthermore, recent studies [14; 15; 16] also emphasize the importance of training data quality for RL-based methods, which is not explored for GUI tasks. However, grounding datasets are typically collected through automated pipelines that extract data from noisy UI accessibility trees or raw HTML, often without thorough verification [7; 6; 8; 17]. This process introduces significant noise, including low-quality instructions (e.g., generic labels like <PushButton>) and bounding boxes corresponding to elements that appear in the DOM but are not visually rendered in the UI. This raises a critical question: How can we assess the quality of each training sample?

To investigate the above problem, we analyze model failures by examining layer attention across the transformer architecture. We found two common patterns: In some cases, the model roughly identifies the target region, but fails to localize it precisely; in others, it completely overlooks the relevant area. These errors often stem from weak alignment between the instruction and the ground truth location.

Motivated by these insights, we propose a Self-Evolutionary reinforcement fine-tuning algorithm that leverages layer attention from a trained model to guide further learning. As grounding accuracy improves, so does the quality of attention, enabling the model to iteratively supervise its own training. At each stage, the best-performing model generates attention maps that inform the next iteration, continuing until performance converges. We summarize our contributions as follows:

Seed Data Curation. We curate a 3,018-sample dataset by filtering out vague, inaccurate, or overly simple tasks from a larger candidate pool. This ensures linguistic consistency and balanced task complexity, promoting better generalization and stable performance across scenarios.

Group Relative Policy Optimization with Dense Point Reward. To combat sparse rewards, we designed a continuous reward mechanism that evaluates the proximity between predictions and ground truth. This provides smoother feedback, enabling the model to learn from near-misses and gradually refine its grounding behavior.

Self-Evolutionary Reinforcement Fine-Tuning. We implement an iterative learning loop, where attention maps serve as intermediate supervision signals. These maps highlight which visual tokens the model attends to for each instruction, helping align its focus with relevant interface elements over time.

We evaluate our SE-GUI on six diverse grounding and agent benchmarks across desktop, mobile, and web environments. As illustrated in Fig. 1, SE-GUI achieves state-of-the-art performance, most notably attaining 47.3% accuracy on the challenging ScreenSpot-Pro benchmark, surpassing the previous best (UI-TARS-72B) by 24.2% at 7B parameter scale with only 3k training samples. These

results demonstrate the effectiveness of our RL-based approach for grounding in complex GUI environments.

2 Related Work

2.1 GUI Agents

GUI agents, as a specialized class of autonomous AI systems, have rapidly evolved with the advent of large foundation models such as LLMs and vision-language models (VLMs) [21; 22; 23], enabling them to interact with graphical user interfaces in increasingly sophisticated ways. Unlike traditional programmatic agents that rely on API calls or internal code access, GUI agents [24; 25; 26; 27; 28] simulate human-like interactions through mouse clicks, keyboard inputs, and visual perception, offering greater flexibility in automating tasks across diverse platforms. Early approaches [29] focused on structured representations like HTML code, but recent advancements [6; 8; 7] demonstrate superior performance when agents directly process visual forms of GUIs, leveraging high-resolution encoders and unified vision-language interfaces . Systems like AppAgent [24; 25], and CogAgent [26] have pioneered this field by enhancing GUI comprehension and interaction precision, while newer frameworks such as UI-TARS [30] and AgentS2 [3] introduce modular architectures and generalistspecialist designs for improved task planning and cross-platform generalization. Despite these strides, challenges remain, particularly in grounding accuracy and data efficiency, as highlighted by methods like UGround [6] and OS-Atlas [7]. However, the reliance on supervised fine-tuning (SFT) poses limitations in scalability and adaptability, motivating the exploration of advanced learning paradigms to overcome the need for vast labeled datasets and improve generalization to unseen interfaces.

2.2 Reinforcement Learning

Reinforcement Learning (RL) has emerged as a transformative approach in the domain of GUI agents [9; 10], leveraging rule-based reward functions to guide model behavior and enhance performance. Frameworks like DeepSeek-R1 [23] have demonstrated the efficacy of RL in tasks such as mathematical reasoning and code generation, with subsequent studies [31; 32] extending its application to multimodal models for visual tasks, including image classification and object detection. as to the application of Reinforcement Fine-Tuning (RFT) in gui tasks. UI-R1 [9] and GUI-R1 [10] represents a pioneering effort in this direction, showcasing the potential of RFT to improve action prediction accuracy and grounding in GUI environments, even with limited data. These developments highlight RL's adaptability and scalability, positioning it as a promising paradigm for future innovations in intelligent GUI agent. However, these approaches typically rely on binary (0/1) rewards, which are often sparse. Such sparsity impedes early-stage training, as incorrect predictions commonly receive identical zero rewards, resulting in uniform advantage estimates and limited gradient information for effective optimization. So, how to design better RL algorithms remains a question worth exploring.

3 Method

In this section, we describe our proposed framework SE-GUI. It comprises three key components as shown in Fig. 2. First, we introduce a data filtering strategy aimed at constructing a clean training set, addressing the need for high-quality data in RL. Second, to mitigate the problem of sparse binary reward signals, we introduce a dense point reward mechanism to guide the model more effectively towards the target even when initial predictions are imprecise. Finally, we propose a self-evolutionary training strategy that iteratively refines the model and data with an attention-based filtering method.

3.1 Seed Data Curation

Our work commences with the collection of diverse open-source grounding datasets including ShowUI [33], UGround [6] and AriaUI [17]. This leads to approximately 300k examples of grounding data encompassing desktop, web, and mobile platforms. These datasets are primarily created through automated pipelines, which often introduce noise, such as inaccurate or off-screen bounding boxes. To address these issues and ensure high training quality, we implemented a three-fold filtering strategy to construct a cleaner and more reliable seed dataset:

IQ:

Instruction Quality

BA:

MLLM

Stage 1: Seed Data Curation

Data Pool

TD:

Task Difficulty

Reference

Model

Rule-based Reward

Prediction

Coordinate:[200,130]

Coordinate:[210,135]

…

Coordinate:[100,300]

Policy

Gradient

KL

Divergence

Dense Point Reward

Format Reward

Attention Guidance

Policy

Model

Repeat Until Convergence

Stage 2:Self- Evolutionary Reinforcement Fine-tuning

BA

IQ

TD

Seed Data

TASK: Search

for app

Click Point: [200,130]

Attention align with

correct UI element ?

TASK:

Show finished audiobook.

Sample

Data

Sample Data

Seed Data

TASK:

Explore children's book.

1.0+

if in box

Rp

1-

otherwise

Discover

Arcade

Arcade

Instruction quality. We use regular expressions to remove low-quality entries such as raw python object names. We then employ a multi-modal large language model to score the remaining samples(The prompt is provided in appendix), evaluating the clarity of the instruction, its alignment with the UI elements, and potential ambiguity.

Bounding box accuracy. Many original annotations are misaligned or include irrelevant components. To correct this, we train a bounding box quality scorer based on the Qwen2.5-VL-7B model. This scorer is fine-tuned on a curated set of high-quality bounding boxes from our collected 100k web pages as positive examples and randomly positioned ones as negatives, allowing it to effectively filter out inaccurate annotations.

Task difficulty. We utilize the Qwen2.5-VL-7B base model to conduct zero-shot testing on each data point, generating 8 output results for each instruction. If all results are correct, the data point is considered too simple and filtered out.

After applying these three filtering steps, we curated a final set of 3,018 high-quality samples, which we refer to as SE-GUI-3k.

3.2 Group Relative Policy Optimization with Dense Point Reward

In our RL training process, the model generates a set of N potential position predictions { o 1 , o 2 , . . . , o N } . Each response is evaluated by format reward and point reward { r 1 , r 2 , . . . , r N } . These rewards are then normalized to calculate the relative advantage of each response. The relative quality A i of the i -th response is computed as

where Mean and Std are the mean and standard deviation of the rewards. This normalization step ensures that responses are compared within the context of the group, allowing model to better capture nuanced differences between candidates. Policy updates are further constrained by minimizing the KL divergence between the updated and reference models, ensuring stable RL learning.

However, during early-stage training, incorrect predictions are common. If we rely solely on binary rewards (e.g., success or failure), the resulting advantage estimates tend to be uniform, providing

limited gradient signals for effective learning. To address the issue of sparse reward feedback, we introduce a dense point reward mechanism. This reward R p is calculated by comparing the predicted click point ( x, y ) with the ground truth bounding box gt bbox = [ x 1 , y 1 , x 2 , y 2 ] . The calculation formula is as follows:

where H and W are image width and height, respectively, d means the normalized distance between the click point and the center point of the ground truth bounding box and d max is the normalized maximum distance between the center point of the ground truth bounding box and the four vertices of the image. Essentially, this formula assigns a reward R p based on whether a predicted point ( x, y ) lies within a bounding box and its normalized distance from the box's center. If the point is inside the box (i.e., x 1 ≤ x ≤ x 2 and y 1 ≤ y ≤ y 2), the base reward is 1 . 0 , and an additional decay term reduces the reward as the point moves farther from the center. If the point is outside the box, the reward only depends on the normalized distance between the point and the box center.

Finally, following previous works [34; 23; 32], we introduce format rewards during training to evaluate whether the generated output adheres to the expected output format. The final response reward is composed of format rewards and point rewards, defined as: R o = αR f + βR Point , where R f represents the format reward, R Point represents the point reward, and α and β are weighting parameters, respectively.

3.3 Self-Evolutionary Reinforcement Fine-Tuning

The proposed algorithm, SE-RFT (Self-Evolutionary Reinforcement Fine-Tuning), outlines a principled approach to iteratively improving a vision-language GUI agent through attention-guided self-supervision. We begin by training an initial model using seed data. In each subsequent training iteration, the model from the previous round is used to generate attention maps for the current training samples. To do this, we generate the model's output sequence and collect self-attention weights from the decoder across all transformer layers. Subsequently, the attention vector for each generated token is normalized, retaining only the components related to the visual tokens. Next, an average is computed across all layers to obtain an aggregated token-to-vision attention weight matrix. Finally, these attention values are projected back onto the original image resolution to produce spatial attention maps. These attention maps highlight the visual tokens on which the current instruction focuses.

If an attention map fails to focus appropriately on the correct UI element, it may indicate that the model lacks relevant prior knowledge or that the sample presents a challenge beyond the model's current capacity . We found that such cases frequently lead to inaccurate localization.

Taking this into account, we propose to guide the loss function with the attention maps. If attention maps cannot correctly attend to the target UI element, these data may lead to incorrect guidance during training, hindering model optimization. To address this, we set the loss for these data points to zero, ensuring that they do not negatively impact the training process. The formula below is used to compute whether the attention map is devoted to the correct target UI element.

where attn means the spatial attention maps, τ is the filtering threshold, P peak indicates if there exists any significant activation point in the target region:

and P global indicates if the average attention weight in the target region is higher than the global average:

where H gt and W gt are grounding truth bounding box width and height, respectively. First, P peak checks if there exists at least one significant activation point within the target region by verifying if any pixel's attention weight exceeds a given threshold τ . Second, P global ensures that the average attention weight within the target region is higher than the global average attention weight across the entire attention map. Together, these criteria ensure that the attention mechanism not only highlights specific points within the target area but also prioritizes the region as a whole compared to the rest of the map. The final loss function is as follows:

where π θ ( a t | s t ) means the current policy, π old ( a t | s t ) means the old policy. The advantage function A t measures the relative value of an action, guiding the optimization process. Additionally, the hyperparameter γ controls the influence of the KL divergence term, KL [ π old ( ·| s t ) , π θ ( ·| s t )] , which penalizes large deviations between the old and new policies, thereby maintaining training stability.

4 Experiments

4.1 Implementation Details

For Reinforcement finetuning, we use the QwenVL2.5-3B/7B [35] model as the base model and train ten epochs. The α , β , γ , τ used in the formula are set as 1, 2, 0.004, and 0.2, respectively. During inference, to ensure fairness, we apply a unified and simple prompt across all benchmarks under zeroshot configurations. All experiments are conducted using 8 × NVIDIA A100-80G GPUs. We evaluate our model on six benchmarks that cover grounding and agents on three different platforms, including ScreenSpot [18], ScreenSpot-v2 [7], ScreenSpot-Pro [4], AndroidControl-Low [19], AndroidControlHigh [19] and OmniAct[20]. Following UGround [6], we use two commonly adopted metrics for GUI agents in evaluation: click point prediction accuracy, and step success rate, denoted as Grounding, and SR, respectively. In more detail, Grounding evaluates the performance of GUI grounding in downstream tasks. Besides, SR represents the step-wise success rate, where a step is deemed successful only if both the predicted action and its associated arguments (e.g., point for click actions and input text for type actions) are correct.

4.2 Experimental Results

We here evaluate our SE-GUI model by comparing it with current state-of-the-art (SOTA) models on various tasks including GUI grounding tasks, GUI low-level tasks, and GUI high-level tasks.

Grounding capability. We evaluate the grounding capability of SE-GUI using ScreenSpot [18], ScreenSpot-v2 [7] and ScreenSpot-Pro [4]. ScreenSpot and ScreenSpot-v2 assesses GUI grounding performance across mobile, desktop, and web platforms, while ScreenSpot-Pro focuses on highresolution professional environments, featuring expert-annotated tasks spanning 23 applications, five industries, and three operating systems.

As shown in Tab. 1, compared to the previous SOTA model UI-TARS-72B, which is trained with large-scale data using supervised finetuning (SFT), our SE-GUI-7B achieves 8.5% improvement using only 0.2% of the data (3K vs. 14M). Furthermore, compared to the base models Qwen2.5-VL7B and the SFT-trained Qwen2.5-VL-7B models using the same dataset, the RFT-based SE-GUI demonstrates much better performance in GUI grounding tasks. Moreover, the results in Tab. 3 reveals that SE-GUI-7B achieves 88.2% on ScreenSpot and 90.25% on ScreenSpot-v2, respectively. This highlights the effectiveness of our method in leveraging small-scale datasets to achieve significant performance improvements, which demonstrates its potential as a data-efficient and scalable approach for model training in resource-constrained environments.

Agent evaluation. We evaluate our method, SE-GUI, on three challenging GUI agent benchmarks: AndroidControl_High, AndroidControl_Low and OmniACT. The AndroidControl benchmark [19] consists of 15K tasks across 833 Android apps. Each task contains a sequence of actions grounded in screenshots and a11y trees, with both high-level intents and optional low-level step-by-step instructions. Following the evaluation protocol in [6], we randomly sample 500 steps from the test split and report step-wise accuracy (SR), where a step is considered correct only if all predicted actions and arguments match the ground truth. As shown in Table 2, SE-GUI-7B significantly outperforms all baselines across both task settings. In the low-level setting, where each step includes a fine-grained instruction, SE-GUI achieves 52.8% accuracy, outperforming GPT-4o (39.4%) and UGround-V1 (48.4%). In the high-level setting, where only the task goal is given, SE-GUI achieves 68.2%, a +5.8% improvement over the strongest baseline, UGround-V1 (62.4%). Notably, unlike prior methods such as UGround-V1 and SeeClick, which are trained on AndroidControl and GUIACT, SE-GUI is trained purely on grounding data, yet generalizes effectively to both task regimes.

For desktop and web environments, we evaluate on the OmniACT dataset [42], which comprises 9,802 tasks spanning 38 applications and 27 websites on macOS, Windows, and Linux. Each task involves generating a complete PyAutoGUI script based on a single screenshot. We follow the DetACT pipeline for a fair comparison, but replace its multimodal detection modules with an MLLM-based grounding system. Specifically, we prompt an MLLM to generate textual descriptions of target UI elements, then use SE-GUI to predict their screen coordinates. These coordinates are composed into scripts using the original prompts and retrieval setup of DetACT, including in-context demonstrations selected by task similarity. On this benchmark, SE-GUI achieves an action score of 34.4, surpassing all other baselines, including GPT-4o (29.6) and UGround-V1 (32.8). These results demonstrate the strong generalization and grounding capabilities of SE-GUI in both mobile and desktop GUI environments, even under zero-shot settings.

4.3 Ablation Study

Image resolution and data quality. To investigate the impact of image resolution and data quality on GUI RFT, we conduct corresponding ablation experiments, with the results shown in Fig. 4(a) and Tab. 5. When training on a filtered, higher quality version of the dataset, the model achieves a 4.45% increase in accuracy. In contrast, using unfiltered, lower-quality data significantly degrades performance, reducing accuracy to just 31.31%. This highlights the importance of data quality in supporting effective model learning. We further examine the effect of image resolution by varying the maximum number of pixels from 1 million to 5 million. As the resolution increases, the model's ability to recognize small but critical UI elements can be improved, leading to steady performance gains. This suggests that higher-resolution inputs provide more detailed visual information, which is especially valuable in complex and professional GUI tasks.

Due to computational constraints, our experiments are limited to a maximum of 5 million pixels. However, the observed trend implies that further improvements in resolution may yield additional performance benefits. Overall, these results emphasize the combined importance of clean, high-quality data and high-resolution visual inputs in advancing GUI grounding capabilities.

The ablation on hyper parameters. As shown in Tab. 4, we observed that the relative weights of the format reward and point reward have a noticeable impact on the final performance. Specifically, setting the ratio of format reward to point reward to 1:1 leads to a slight performance drop. To address this, we adjusted the weight ratio to 1:2 during training. We attribute this to the relative simplicity of

1.0

0.8

0.6

0.4

0.2

(a) Pin Jack's conversation.

(b) Initiate a video call with Ash.

learning format consistency; reducing its weight encourages the model to focus more on prediction accuracy.

In addition, the KL divergence coefficient also influences model performance. Our experiments show that a smaller KL divergence leads to better results. We hypothesize that a large KL term forces the model to overly align with the reference policy, thereby limiting its capacity for exploratory learning. Consequently, we set the KL divergence coefficient to 0.004 in our final experimental setup.

We also conducted detailed ablation studies on the attention map filtering threshold. When the threshold is set too high, it may filter out samples with potential for reasoning. Conversely, when the threshold is too low, it fails to provide effective supervisory guidance. Therefore, we selected a threshold of 0.2.

The effectiveness of dense point reward function. To explore the impact of the coefficients for format rewards and accuracy rewards in the reward function on the final performance, we conduct relevant ablation experiments, as shown in Table 5. The results indicate that, compared to sparse rewards, dense rewards bring a 4.21% improvement . This is because dense rewards allow the model to receive more reward signals during the early stages of training, thus guiding the model to focus on the correct positions of UI elements.

The effectiveness of self-evolutionary reinforcment fine-tuning. To investigate the impact of self-evolutionary training on the final performance, we conducted relevant ablation experiments, as shown in Fig. 4(b). The results indicate that the first-stage model, trained solely on the filtered dataset SE-GUI-3k, achieved a performance of 39.97%. Subsequently, by leveraging the pre-trained model from the first stage to generate attention maps for supervising the training of the second-stage model, the performance improved by 3%. Continuing this process, where each subsequent stage utilizes the previous stage's model to generate attention maps for supervision, the third-stage model achieved a performance of 46.55%. In the fourth stage, the improvement was minimal, indicating that the model's performance had largely converged.

Furthermore, we visualize the reward curves for each training stage to better illustrate the learning dynamics. The curves are smoothed to clearly reveal the overall training trends and convergence behavior. As shown in Fig. 4(c), with the progression of training, each subsequent stage in the self-evolutionary process consistently achieves higher rewards compared to its predecessor. The final

stage's reward curve closely aligns with that of the preceding stage, indicating that the model has almost converged.

In addition, as shown in Fig. 4(d), we observe that on the ScreenSpot-Pro dataset, the previous state-of-the-art model, UI-TARS-72B, performs poorly in certain specialized scenarios, with accuracy dropping below 10%. In contrast, our model demonstrates strong generalization capabilities, achieving significant improvements in these challenging settings. Specifically, it boosts accuracy by nearly 40% on SolidWorks (SW) and Inventor (INV), which validates the effectiveness of our approach.

2k

3k

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Response Reward

Training Iteration

Stage 1

Stage 2

Stage 3

Stage 4

40

20

10

Accuracy (%)

CAD

SW

INV

Professional Environments

30

11.8

35.3

2.6

39.0

4.3

41.4

SE-GUI-7B

UI-TARS-72B

50

(d)

(c)

45

35

30

ScreenSpot-Pro Accuracy (%)

1

2

3

4

Training Stage

40

31.9

39.9

33.0

42.1

35.7

46.5

35.8

46.6

7B

3B

50

33

31

30

1

2

3

4

Max pixel(10

6

)

32

34

32.8

30.2

35.4

35.9

ScreenSpot-Pro Accuracy (%)

(a)

(b)

Figure 4: More experiments. (a) Perfromance of SE-GUI-3B improves as max pixel increases. (b) Performance in each training stage of self-evolutionary process. (c) The reward curves in each training stage of SE-GUI-7B in self-evolutionary process. (d) Performance comparison between UI-TARS-72B and our SE-GUI-7B in professional environments.

Attention visualization. We present several visualizations of the model's attention behavior. During the visualization process, we compute the attention weights from the generated tokens to these visual tokens. The attention values corresponding to the visual tokens are then mapped back to the original image resolution, yielding a spatial attention map that reflects how the model attends to different regions of the image. As shown in Fig. 3, given the user instruction "pin Jack's conversation", the model correctly attends to the upper-right region of the image. When prompted with the instruction "initiate a video call with Jack," the attention map on the right fails to localize the ground truth region, likely due to its inability to recognize the corresponding "video call" icon.

5 Conclusions and Limitations

Conclusions. In this work, we explore how to more effectively leverage reinforcement learning to unlock the potential of large multimodal models as GUI Agents. Motivated by recent studies that emphasize the importance of training data quality for RL-based methods, we employ a data filtering strategy to curate a high-quality dataset, on which we train a base model. Building upon this, we adopt a self-evolutionary reinforcement fine-tuning paradigm to progressively enhance model performance. Our approach achieves state-of-the-art results across three grounding benchmarks. We hope this study provides new insights for future research in the field of GUI Agents.

Limitations and future work. Due to hardware limitations, this paper explores only two model scales: 3B and 7B. For the 7B model, we further constrained the maximum input resolution to 2 million pixels to limit the number of visual tokens, which may result in the loss of fine-grained details in high-resolution images. Nevertheless, we believe that our method exhibits strong scalability and generalization capabilities, and has the potential to achieve even better performance when applied to larger models such as 32B or 72B.

References

Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxiao Dong, Ming Ding, et al. Cogagent: A visual language model for gui agents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 14281-14290, 2024.

Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, et al. Ui-tars: Pioneering automated gui interaction with native agents. arXiv preprint arXiv:2501.12326 , 2025.

Saaket Agashe, Kyle Wong, Vincent Tu, Jiachen Yang, Ang Li, and Xin Eric Wang. Agent s2: A compositional generalist-specialist framework for computer use agents, 2025.

Kaixin Li, Ziyang Meng, Hongzhan Lin, Ziyang Luo, Yuchen Tian, Jing Ma, Zhiyong Huang, and Tat-Seng Chua. Screenspot-pro: Gui grounding for professional high-resolution computer use. Workshop on Reasoning and Planning for Large Language Models , 2025.

Shravan Nayak, Xiangru Jian, Kevin Qinghong Lin, Juan A Rodriguez, Montek Kalsi, Rabiul Awal, Nicolas Chapados, M Tamer Özsu, Aishwarya Agrawal, David Vazquez, et al. Uivision: A desktop-centric gui benchmark for visual perception and interaction. arXiv preprint arXiv:2503.15661 , 2025.

Boyu Gou, Ruohan Wang, Boyuan Zheng, Yanan Xie, Cheng Chang, Yiheng Shu, Huan Sun, and Yu Su. Navigating the digital world as humans do: Universal visual grounding for gui agents. arXiv preprint arXiv:2410.05243 , 2024.

Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang, Qiushi Sun, Chengyou Jia, Kanzhi Cheng, Zichen Ding, Liheng Chen, Paul Pu Liang, et al. Os-atlas: A foundation action model for generalist gui agents. arXiv preprint arXiv:2410.23218 , 2024.

Yiheng Xu, Zekun Wang, Junli Wang, Dunjie Lu, Tianbao Xie, Amrita Saha, Doyen Sahoo, Tao Yu, and Caiming Xiong. Aguvis: Unified pure vision agents for autonomous gui interaction. arXiv preprint arXiv:2412.04454 , 2024.

Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin, Liang Liu, Hao Wang, Guanjing Xiong, and Hongsheng Li. Ui-r1: Enhancing action prediction of gui agents by reinforcement learning. arXiv preprint arXiv:2503.21620 , 2025.

Xiaobo Xia and Run Luo. Gui-r1: A generalist r1-style vision-language action model for gui agents. arXiv preprint arXiv:2504.10458 , 2025.

Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems , 36:53728-53741, 2023.

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 , 2017.

Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 , 2024.

Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. Limo: Less is more for reasoning. arXiv preprint arXiv:2502.03387 , 2025.

Xuefeng Li, Haoyang Zou, and Pengfei Liu. Limr: Less is more for rl scaling. arXiv preprint arXiv:2502.11886 , 2025.

Chong Zhang, Yue Deng, Xiang Lin, Bin Wang, Dianwen Ng, Hai Ye, Xingxuan Li, Yao Xiao, Zhanfeng Mo, Qi Zhang, et al. 100 days after deepseek-r1: A survey on replication studies and more directions for reasoning language models. arXiv preprint arXiv:2505.00551 , 2025.

Yuhao Yang, Yue Wang, Dongxu Li, Ziyang Luo, Bei Chen, Chao Huang, and Junnan Li. Aria-ui: Visual grounding for gui instructions. arXiv preprint arXiv:2412.16256 , 2024.

Kanzhi Cheng, Qiushi Sun, Yougang Chu, Fangzhi Xu, Yantao Li, Jianbing Zhang, and Zhiyong Wu. Seeclick: Harnessing gui grounding for advanced visual gui agents. arXiv preprint arXiv:2401.10935 , 2024.

Wei Li, William Bishop, Alice Li, Chris Rawles, Folawiyo Campbell-Ajala, Divya Tyamagundlu, and Oriana Riva. On the effects of data scale on computer control agents. arXiv preprint arXiv:2406.03679 , 2024.

Raghav Kapoor, Yash Parag Butala, Melisa Russak, Jing Yu Koh, Kiran Kamble, Waseem AlShikh, and Ruslan Salakhutdinov. Omniact: A dataset and benchmark for enabling multimodal generalist autonomous agents for desktop and web. In ECCV , pages 161-178. Springer, 2024.

An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 , 2024.

Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923 , 2025.

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 , 2025.

Chi Zhang, Zhao Yang, Jiaxuan Liu, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents as smartphone users. arXiv preprint arXiv:2312.13771 , 2023.

Yanda Li, Chi Zhang, Wanqi Yang, Bin Fu, Pei Cheng, Xin Chen, Ling Chen, and Yunchao Wei. Appagent v2: Advanced agent for flexible mobile interactions. arXiv preprint arXiv:2408.11824 , 2024.

Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxiao Dong, Ming Ding, et al. Cogagent: A visual language model for gui agents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 14281-14290, 2024.

Junyang Wang, Haiyang Xu, Jiabo Ye, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent: Autonomous multi-modal mobile device agent with visual perception. arXiv preprint arXiv:2401.16158 , 2024.

Junyang Wang, Haiyang Xu, Haitao Jia, Xi Zhang, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent-v2: Mobile device operation assistant with effective navigation via multi-agent collaboration. arXiv preprint arXiv:2406.01014 , 2024.

Hao Wen, Yuanchun Li, Guohong Liu, Shanhui Zhao, Tao Yu, Toby Jia-Jun Li, Shiqi Jiang, Yunhao Liu, Yaqin Zhang, and Yunxin Liu. Autodroid: Llm-powered task automation in android. arXiv preprint arXiv:2308.15272 , 2023.

Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, et al. Ui-tars: Pioneering automated gui interaction with native agents. arXiv preprint arXiv:2501.12326 , 2025.

Haozhan Shen, Zilun Zhang, Kangjia Zhao, Qianqian Zhang, Ruochen Xu, and Tiancheng Zhao. Vlm-r1: A stable and generalizable r1-style large vision-language model. https: //github.com/om-ai-lab/VLM-R1 , 2025. Accessed: 2025-02-15.

Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Yao Hu, and Shaohui Lin. Vision-r1: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749 , 2025.

Kevin Qinghong Lin, Linjie Li, Difei Gao, Zhengyuan Yang, Zechen Bai, Weixian Lei, Lijuan Wang, and Mike Zheng Shou. Showui: One vision-language-action model for generalist gui agent. In NeurIPS 2024 Workshop on Open-World Agents , 2024.

Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, et al. Mm-eureka: Exploring visual aha moment with rule-based large-scale reinforcement learning. arXiv preprint arXiv:2503.07365 , 2025.

Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923 , 2025.

Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276 , 2024.

Anthropic. Developing a computer use model. https://www.anthropic.com/news/ developing-computer-use , 2024. Accessed: 2025-04-12.

Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang, Qiushi Sun, Chengyou Jia, Kanzhi Cheng, Zichen Ding, Liheng Chen, Paul Pu Liang, et al. Os-atlas: A foundation action model for generalist gui agents. arXiv preprint arXiv:2410.23218 , 2024.

Boyu Gou, Ruohan Wang, Boyuan Zheng, Yanan Xie, Cheng Chang, Yiheng Shu, Huan Sun, and Yu Su. Navigating the digital world as humans do: Universal visual grounding for gui agents. arXiv preprint arXiv:2410.05243 , 2024.

Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin, Liang Liu, Hao Wang, Guanjing Xiong, and Hongsheng Li. Ui-r1: Enhancing action prediction of gui agents by reinforcement learning. arXiv preprint arXiv:2503.21620 , 2025.

OpenAI. Gpt-4o, 2024. Accessed: 2025-01-03.

Raghav Kapoor, Yash Parag Butala, Melisa Russak, Jing Yu Koh, Kiran Kamble, Waseem Alshikh, and Ruslan Salakutdinov. Omniact: A dataset and benchmark for enabling multimodal generalist autonomous agents for desktop and web. arXiv preprint arXiv:2402.17553 , 2024.

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 , 2025.

Weihao Zeng, Yuzhen Huang, Wei Liu, Keqing He, Qian Liu, Zejun Ma, and Junxian He. 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient. https://hkust-nlp.notion.site/simplerl-reason , 2025. Notion Blog.

Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, and Jiaqi Wang. Visual-rft: Visual reinforcement fine-tuning. arXiv preprint arXiv:2503.01785 , 2025.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Our main claims made in the abstract and introduction accurately reflect the paper's contributions and scope.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: The paper discusses the limitations in the Conclusion section.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: The paper provides a complete set of theoretical results, each accompanied by clearly stated assumptions and complete proofs.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: The paper fully discloses all the information necessary to reproduce the main experimental results (in main submission and appendix), ensuring that the main claims and conclusions of the paper can be independently verified and validated.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [No]

Justification: The paper has not yet been open-sourced for data and code.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: We provide a thorough description of the details of our experiments in the paper and appendix.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [NA]

Justification: There is no reporting of error bars or statistical significance information.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [No]

Justification: : The paper does not provide sufficient information on the computer resources, such as the type of compute workers, memory, and time of execution needed to reproduce the experiments.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: The research conducted in the paper conforms to the NeurIPS Code of Ethics, as outlined in the provided URL. The paper adheres to the ethical practices and guidelines specified in the NeurIPS Code of Ethics during the research process.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [No]

Justification: The paper solely emphasizes the positive societal impacts of the work performed, omitting any discussion of potential negative consequences or societal drawbacks.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: The paper poses no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: The paper thoroughly acknowledges and properly credits the creators or original owners of assets, including code, data, and models, used in the research. Additionally, it explicitly mentions and respects the licenses and terms of use associated with these assets, ensuring ethical and legal compliance.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: The paper curate a final set of 3,018 high-quality samples refered to as SEGUI-3k.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [Yes]

Justification: We adopt Qwen2.5-vl as the base model in constructing our SE-GUI.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

A GRPO preliminary

Many rule-based RL works [43; 44; 45] adopt the Group Relative Policy Optimization (GRPO) algorithm [13] for RL training. GRPO offers an alternative to commonly used Proximal Policy Optimization (PPO) [12] by eliminating the need for a critic model. Instead, GRPO directly compares a group of candidate responses to determine their relative quality.

In GRPO, given a task question, the model generates a set of N potential responses { o 1 , o 2 , . . . , o N } . Each response is evaluated by taking the corresponding actions and computing its reward { r 1 , r 2 , . . . , r N } . Unlike PPO, which relies on a single reward signal and a critic to estimate the value function, GRPO normalizes these rewards to calculate the relative advantage of each response. The relative quality A i of the i -th response is computed as

where Mean and Std represent the mean and standard deviation of the rewards, respectively. This normalization step ensures that responses are compared within the context of the group, allowing GRPO to better capture nuanced differences between candidates. Policy updates are further constrained by minimizing the KL divergence between the updated and reference models, ensuring stable RL learning.

B More training details.

Due to resource constraints, during training on the 7B model, we limited the maximum input resolution to 2 million pixels. We believe that increasing this limit could lead to further performance improvements. Below, we present several representative prompts used during training, along with the complete reward curves, as shown in Fig. 5.

Bounding box accuracy scoring prompt.

Analyze the provided cropped image from a screenshot to determine whether it contains a single, valid, and visually complete UI element. Criteria for validity:

- The image must contain exactly one UI element. - The element must be entirely visible within the cropped area, with no significant cut-off parts. - The image should not consist solely of background, empty space, or meaningless fragments.

Response format:

Conclude with your final determination in a dedicated section:

Conclusion Yes (if the image contains a single, valid, and complete UI element) No (if it does not meet the criteria)

Instruction quality scoring prompt.

Analyze whether the instruction text precisely identifies the UI element in the image based on: 1. Does the instruction EXACTLY match visible text/core function? 2. Could the instruction confuse similar elements in context? 3. Does it clearly indicate the required action without ambiguity?

Instruction to evaluate: instruction

Conclude with your final determination:

Conclusion Yes (if all criteria are met) No (if any criterion fails)

Training Prompt.

You

are

a

helpful

assistant.

You

may

call

one

or

more

functions

to

assist

with

the

user

query.

You

are

provided

with

function

signatures

within

<tools ></tools >

XML

tags:

<tools >

{"type":

"function",

"function":

{"name_for_human":

"computer_use",

"name":

"computer_use",

"description":

"Use

a

and

keyboard

to

interact

with

a

computer ,

and

take

screenshots.

*

This

is

an

interface

to

a

desktop

GUI.

You

do

not

have

access

to

a

terminal

or

applications

menu.

You

must

click

on

desktop

icons

to

start

applications.

*

Some

applications

may

take

time

to

start

or

process

actions ,

so

you

may

need

to

wait

and

take

successive

screenshots

to

see

the

results

of

your

actions.

*

The

screen 's

resolution

is

1876x1036.

*

Whenever

you

intend

to

move

the

cursor

to

click

on

an

element

like

an

icon ,

you

should

consult

a

screenshot

to

determine

the

coordinates

of

the

element

before

moving

the

cursor."}}

</tools >

For

each

function

call ,

return

a

object

with

function

name

and

arguments

within

<tool_call ></tool_call >

XML

tags:

<tool_call >

{"name":

<function -name >,

"arguments":

<args-json-object >}

</tool_call >

train/rewards/point_reward

1.5

0.5

train/global_step

1k

2k

3k

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents SE-GUI, a reinforcement learning-based framework designed to enhance visual grounding capabilities in GUI agents. The authors propose three key components: (1) a curated dataset of 3,018 high-quality samples to ensure effective training, (2) a novel dense point reward mechanism to provide continuous feedback rather than sparse binary signals, and (3) a self-evolutionary reinforcement fine-tuning strategy that iteratively refines the model using attention maps from previous stages. Despite using significantly fewer training samples, SE-GUI achieves state-of-the-art performance on multiple benchmarks, including outperforming much larger models like UI-TARS-72B on the challenging ScreenSpot-Pro dataset. The work demonstrates the effectiveness and data efficiency of reinforcement learning for complex GUI grounding tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nInnovative self-evolutionary fine-tuning strategy\nThe paper introduces a novel self-evolutionary reinforcement fine-tuning approach that leverages attention maps for iterative training, enabling the model to progressively improve itself with minimal human supervision.\n\nData efficiency\nSE-GUI achieves strong performance using only 3k high-quality training samples, outperforming much larger models like UI-TARS-72B, which demonstrates excellent data efficiency and scalability.\n\nComprehensive experiments and ablation studies\nThe paper includes detailed ablations and evaluations, validating the contributions of each component, including seed data quality, reward function design, and the self-evolutionary loop.\n\nWeaknesses\n\nOne potential concern is the reliance on Qwen2.5-VL-7B for filtering low-quality samples during the seed data curation process. However, Qwen2.5-VL-7B itself is not particularly strong in GUI grounding, as evidenced by its relatively modest performance in the paper’s own benchmark results. This raises questions about the reliability of using it as a reference model for data quality assessment—particularly whether the filtered dataset truly reflects accurate grounding targets or inadvertently reinforces the biases and limitations of the filtering model.\n\nother see question？\n\nQUESTIONS:\n1.The seed data is filtered using Qwen2.5-VL-7B, but this model itself shows limited GUI grounding performance in your benchmarks. How can we be confident that the filtered data is truly reliable and not biased by the limitations of the filtering model?\n\n2. The paper shows that combining small high-quality datasets with RL yields strong results for 3B and 7B models. A natural and important question is whether this approach scales to larger models like Qwen2.5-VL-32B. While we understand the computational cost, verifying this scaling law would be highly valuable and could strengthen the generality of the method.\n\n3. Would scaling up the amount of high-quality filtered data further improve performance? Have the authors observed diminishing returns, or is there evidence that more clean data could lead to even greater gains?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an RL-based method to turn a VLM foundation model into a GUI agent for complex UI grounding tasks.\nThe core of the proposed method comprises a dataset filtering pipeline, a reward function that measures point distance for the GUI agent task, and a self-evolutionary scheme containing a modified loss function with an additional filtering term calculated based on the attention map of the previous-iteration model.\n\nExperiments in this paper report that the proposed method significantly outperforms the SOTA GUI agent models with both comparable scales (7B) and much larger scales (72B) on ScreenSpot-Pro benchmark.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n\nThe filtering dataset has higher quality than existing ones, which should help the research community if authors disclose it.\nThe proposed method is task-specific and reasonable, and the performance improvement reported in this paper looks significant.\n\n**Weakness**\n\n- **Lack of Ablation**\n\nAlthough ablation of the point reward function and the self-evolutionary tuning are provided, the authors did not conduct an ablation study on the term $f(\\text{attn}, gt^{\\text{bbox}}, \\tau)$ in Equation 7. This term is the core difference between this proposed method and the previous GRPO method. \n\n- **Lack of Visual Samples to Support Numeric Results**\n\nAlthough the numeric results in Table 3 show that the proposed method almost outperforms previous SOTA, no actual visualized samples are provided to elaborate on this claim. In which cases do previous SOTA methods tend to fail, and does the proposed method solve effectively? These results are necessary to support the experimental conclusion.\n\n- **Selective Reveal of Numeric Performance of SOTA Methods**\n\nAlthough the proposed method (7B) outperforms UI-TARS (2B, 7B, and 72B) on ScreenSpot-Pro benchmark, it does not outperform UI-TARS (7B and 72B) on the the ScreenSpot and ScreenSpot-v2 benchmark. \n\nThis can be validated by comparing the submitted paper and the original paper of UI-TARS (https://arxiv.org/pdf/2501.12326).\nAccuracy results of UI-TARS (2B) on ScreenSpot and ScreenSpot-v2 in Table 3 are directly taken from the original paper, but authors just skipped showing the results of UI-TARS 7B and 72B, which are higher than the proposed method. \n\n- **Unclear Presentation**\n\nThere are some ambiguous points in writing.\nDoes $R_{\\text{Point}}$ in L155 refer to $R_p$ in Equation 3? Appendix Figure 6 is not referred to. It would be better to mention it in the related text part.\n\nQUESTIONS:\nFollowing the weakness part:\n\n1. How does the attention-map-based term in the loss function affect the model's performance? An ablation is needed.\n\n2. In which cases do previous SOTA methods fail, and does the proposed method work? Several visualized results showing the superior performance of the proposed method are needed.  \n\n3. I would like to know the reason why authors chose not to reveal the results of UI-TAR (7B and 72B) on the ScreenSpot and ScreenSpot-v2 benchmark in Table 3 while showed these results in Table 1?\n\n4. If possible, I suggest the authors improve the overall presentation of the paper and fix the dangling figure items and typos.\n\nLIMITATIONS:\nA limitation of this method is the lack of evaluation with scalability to larger models, which is briefly discussed by the authors in Section 5.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Self-Evolutionary Reinforcement Fine-Tuning and builds\nSE-GUI to perform visual grounding in GUI tasks. It cleans the popular\ngrounding training sets, designs dense point reward for grounding task, and\nexploits the attention maps to filter training samples. The experiment results\nprove the effectiveness of SE-GUI.\n\nThe method is feasible. The presentation is clear. The evaluation is sufficient\nto me.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. Dense point reward is adopted for RL training in grounding tasks,\n   alleviating the reward sparsity in early training.\n2. Attention maps in the model are exploited innovatively to filter training\n   samples.\n3. Impressive performance boost is obtained.\n\nI don't see significant weaknesses. Some minor problems and questions are listed in Questions.\n\nQUESTIONS:\n**About the method**\n\n1. In the current version, the attention-map-based score serves as a constant\n   coefficient of the final loss. I wonder what if you consider it a component\n   in the composite reward. Did you consider this manner?\n\n**About the experiments**\n\n1. In Table 4, the performances on Creative and Scientific categories drop when\n   using dense reward both with and without data filtering. What's the reason?\n   Similarly, in Table 1, the performance of icon grounding on Creative\n   category is remarkably lower. What's the reason? Is there some special\n   difficulties for Creative scenarios?\n2. The results of UI-TARS-7B/72B and SE-GUI-3B are missed in Table 3.\n\n**Other questions**\n\n1. On L131, \"format reward and point reward $\\{r_1, r_2, \\dots, r_N\\}$\" results\n   in ambiguity. Does $r_i$ mean the sum of format and point rewards or just\n   the point reward?\n2. On L205, \"input text for scroll actions\" is strange.\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 studies the screenshot grounding task, of particular relevance to UI agents.  The authors propose a new method for determining a high-quality SFT dataset, a method for applying GRPO to the task in order to benefit from dense supervision, and a \"self-evolutionary reinforcement finetuning mechanism\" that leverages information in the Transformers' internal attention maps to iteratively refine the set of considered examples. The recipe results in a 7B model that achieves strong performance on the competitive ScreenSpot-Pro benchmark.\n\nSTRENGTHS AND WEAKNESSES:\n__Strengths__\n\n* The application of GRPO to screenshot grounding with dense rewards seems reasonable.\n* The data filtering strategies similarly seem reasonable, and the resulting dataset could be useful to the community.\n* The proposed approach demonstrates strong performance on competitive benchmarks.\n* Ablations seemingly demonstrate the effectiveness of each individual component of the proposed recipe.\n* The paper was well structured and relatively easy to follow.\n\n__Weaknesses__\n\n* I found the proposed \"Self-Evolutionary Reinforcement Fine-Tuning\", which leverages the information in the Transformer's attention maps for identifying problematic examples, to be less intuitively compelling than other aspects of the method. It might have been interesting to consider alternative, more general (e.g. architecture-agnostic) baseline for identifying examples where the model lacks appropriate prior knowledge, such as cases where all samples from the model receive reward below some threshold?\n* Relatedly, for the ablation of the proposed \"Self-Evolutionary Reinforcement Fine-Tuning\", would an appropriate baseline need to be trained for the same number of steps as all stages combined?\n\n__Notes__\n\n* The ScreenSpot-Pro leaderboard lists several models with stronger performance, but most of these appear to be concurrent work.\n* I would consider increasing my score based on discussion with the authors and other reviewers related to the proposed \"Self-Evolutionary Reinforcement Fine-Tuning\" method.\n\nQUESTIONS:\nSee weaknesses above.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}