SE-GUI: Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning
Authors: Xinbin Yuan
Jian Zhang
Kaixin Li
Zhuoxuan Cai
Lujian Yao
Jie Chen
Enguang Wang
Qibin Hou
Jinwei Chen
Peng-Tao Jiang
Bo Li
Venue: NeurIPS 2025 poster ()
Keywords: gui agent; reinforcement learning; visual grounding
Primary area: applications

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
Xinbin Yuan 1.2- Jian Zhang
Kaixin Li
Zhuoxuan Cai²
Lujian Yao?
Jie Chen
Enguang Wang² Qibin Hou Jinwei Chena Peng-Tao Jiang?
Bo Li
VCIP, School of Computer Science, NKU
vivo Mobile Communication Co., Ltd 3 National University Singapore
yxb @ mail.nankal .en....euaiii..n edu.cn, hougb @ nankai.edu.cn. libra @ civo.com Project page: https:/ / thub. com/YXB YXB-NKU//EE-CTTTT
Abstract
Graphical User Interface (GUI) agents have made substantial strides in under- standing and executing user instructions across diverse platforms. Yet, grounding eespciaal these instructions to precise interface elements remains Traditional supervised in complex, high-resolution, professional environments. fine-tuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce reinforcement learning (RL))based framework that incorporates three core strategies: (1) seed data curation ensure high-quality training samples, (2) a dense policy gradi- ent 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 the ScreenSpot-Pre dataset-oul outperfrrnngg 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.
Introduction
Graphical User Interface (GUI) agents have become increasingly capable of executing commands across diverse platforms [1; 2]. Yet, a core challenge remains: accurately grounding natural language instructions to the correct elements has been proven to be effective in simple scenarios [6: 7: 8), faces two major limitations: requiring large volumes of diverse data and exhibiting weak generalization in complex and high-resolution professional settings [9; 10].
the interface [3; 4: 5]. While Supervised Fine-Tunine (SFT)
To address these issues, reinforcement learning (RL) offers promising alternative. Unlike SFT, RL enables models to learn from structured and incremental feedback via reward functions, guiding them toward more precise grounding. Among RI--based methods [11; 12; 13], Group Relative Policy Optimization (GRPO) [13] stands out for its efficiency. replaces heavy value models with a simple
"This work was done during Xinbin Yuan's 's internship at vivo.
*Project lead.
Correspponing author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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AC ACAlle
CC-4444
UI-TARS-728
SE-c SE-GUT-18
C--TARS-78
X
35
aCta
Qheek 5-V2-25
08-4da--78
Clande
M500 Wab
ShowUV-28
(hon2-911-28
1888
Madel Paramnatooy
Figure 1: Performance comparison of various GUI agents both the grounding benchmark (SoreenSpot [18], ScreenSpot-v2 [7], SereenSpot-Prye [4]) and the agent benchmark (Android Control- High [19], Android_ Lesrdd_Cotooo--ii Low [19], OmniACT [20]). Our model, SE-GUI marked with star, demonstrates statc-of-the-at performance against models with larger parameter counts.
rule-based reward system, reducing the computational burden. However, typical approaches [9; 10] rely on binary (0/1) rewards, which 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 training data quality for RL-based methods, which is not explored for GUI tasks. However, grounding datasets typically collected through automated pipelines that extract data from noisy 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 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, 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 Eollutvvlutionaryy reinforcement fine-tuning algorithm that leverages layer attention from trained model to guide further learning. As grounding accuracy improves, so does the quality attention, enabling the model 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:
1. Seed Data Curation. We curate a 3,018 s-smmll 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.
2. Group Relative Policy Optimization with Dense Point Reward. To combat sparse rewards, 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.
3. Self-Evolutionary Reinforcement Fine-Tuning We implement 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.
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-arr performance, most notably attaining 47.3% accuracy on the challenging ScreenSpot- benchmark, surpassing the previous best 2L1-TARS-72D by 24.2% at 7B parameter scale with only 3k training samples. These

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results demonstrate the effectiveness of our RL-based approach for grounding in complex GUI environments.
Related Work
2.1
GUI Agents
GUI agents, as specialized class of autonomous Al systems, have rapidly evolved with the advent of
large foundation models such as LLMs and vision-langure models (VLMs) [21; 22; 23], enabling them to interact with graphical user interfaces in increasingly sophisticated ways. Unlike traditional programmatic agents that rely API calls 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
[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 generalist- specialist designs for improved task planning and cross pooss-plaform generalization. Despite these strides, challenges remain, particularly in grounding accuracy and data efficiency, as highlighted by methods like UGround [6] and OS-Atlan [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 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 perfor- mance. Frameworks like DeepSeek-RI R1 [23] have demonstrated the efficacy of RL. in tasks such as mathematical reasoning and code generation, with subsequent studies [31; 32] extending its applica- tion 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-RI [9] and GUI-RI [10] represents 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 promising paradigm for future inno- vations in intelligent GUI agent. However, these approaches typically rely on binary (0/1) rewards, which are often sparse. Such sparsity impedes carly-stage training, incorrect predictions com- monly receive identical zero rewards, resulting in uniform advantage estimates and limited gradient information for effective optimization. So, how design better RL algorithms remains question worth exploring.
3 Method
In this section, we describe our proposed framework SE-GUI. 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 RL. Second, to mitigate the problem of sparse binary reward signals, we introduce dense point reward mechunism guide the model more effectively towards the target even when initial predictions are imprecise. Finally, we propose 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 ShowUl [33], UGround [6] and [ri] 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 fhree-fold filtering strategy to construct a cleaner and more reliable seed dataset:
3

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Stage 1: Seed Data Curation TASK: Skow fensked axdinbook.
TD
Sample Data
BA: Box Accurncy
IQ: Instraction Quality
TASK: Suster oliMes' Josa
Duta Pool
MILM
TD: Task Difficulty
Sood Data
Stage 2:Self- Evolutionary Reinforcement Fine-tuning
ii box
Rg
otherwiee
Seed Data
Referenc: Model
Rule-bused Reward
Atteunox alie with
Sample Data
Prodiciis
KZ Divergonce
Policy Gradient
Conalwar (300115
Search for apo
Fonrat Revaad
Policy
Coosloan (100 H83
Aftentinn Giidenco
Repeat Until Convergence
Click Poter (30)
Figure Overview of the SE-GUI Framework. Given high-level instructions and the corresponding screenshots, SE-GUI generates multiple candidate responses and is optimized via GRPO using verifiable rewards as feedback signals. The updated model then leveraged to identify high-quality samples in the training set, which yields cleaner learning signal for subsequent iterations. This iterative process enables SE-GUI to progressively improve itself by refining its predictions and training data.
Instruction quality. We use regular expressions to remove low-quality entries such raw python ob- ject 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 model. This scorer is fine-tuned on curated set of high-que high-quality bounding boxes from our collected 100k web pages as positive examples and randomly positioned ones as negatives, allowing effectively filter out inaccurate annotations.
Task difficulty. We utilize the Qwen2.5-VI-7B base model to conduct zero-shot testing 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 refer to SE-GUI-3k.
3.2 Group Relative Policy Optimization with Dense Point Reward
In our RL training process, the model generates set of N potential position predictions {01, o2...., ON) ). Each response is evaluated by format reward and point reward [21,72.... FN }. These rewards are then normalized to calculate the relative advantage of each response. The relative quality A, of the i-th response is computed as
A; Ti Mean (rr,
Sid((ri, (2,,,,
where Mcan and Std are the mean and standard deviation of the rewards. This normalization step ensures that responses compared within the context of the group, allowing model to better capture nuanced differences between candidates. Policy updates are further constrained by minimizing the KT. divergence between the updated and reference models, ensuring stable RL learning-
However, during early-stage training, incorrect predictions are common. we rely solely on binary rewards (c.g., success failure), the resulting advantage estimates tend to be uniform, providing

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limited gradient signals for effective learning. To address the issue of sparse reward feedback, we introduce a dense point reward mechanism. This reward Rp is calculated by comparing the predicted click point (x. y) with the ground truth bounding box gr [21, y1, T2, y2). The calculation formula is as follows:
21
+ 22
31 + U2 2 H
+
(2)
W
2
W
H
1
1.0 + (1-
31 S x S 12
if
Rg =
U1 S V S 122
2
d dass
otherwise
where 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 dmnar 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 reward R, based on whether a predicted point (ær, y) lies within a bounding box and its normalized distance from the box's 's center. If the point is inside the box (i.e., 21 S x S 22 and y/1 S y S 1/2), 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. = a aR + 3 Roim: where Rt represents the format reward, Rpoint represents the point reward, and a and 3 are weighting parameters, respectively.
3.3 Self-Evolutionary Reinforcement Fine-Tuning
The proposed algorithm, SE-RFT Self-EEvoutionnrry Reinforcement Fine- Fine-Tuning), outlines a prin- cipled approach to iteratively improving vision-language GUI agent through ivcentiongguider self-supervision. We begin by training an initial model using seed data. 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 normalized, retaining only the components related to the visual tokens. Next, an average is computed across all layers to obtain aggregated token-to-vie to-visioon 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 which the current instruction focuses.
an attention map fails to focus appropriately on the correct element, it may indicate that the model lacks relevant prior knowledge or that the sumple presents a challenge beyond the model's 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 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.
1,
Ppeak A Pglobal
(attn, gl bbox T)
0, otherwise
where alin means the spatial attention maps, 7 is the filtering threshold, Ppeak indicates if there exists any significant activation point in the target region:
P Ppeak = 1
max ( x ((an attn i [+]]>>]]))))
>T)
(5)
5

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and Pglobal indicates if the average attention weight in the target region is higher than the global average:
1/2 attn [1,
M--w-- Σ T=
Pglohal = 1
Σ 1=
atto atm]i;,]]
(6)
Hgt W gt 1= 1=
HW
where Hg: and Wgr are grounding truth bounding box width and height, respectively. First, Prea checks if there exists at least one significant activation point within the target region by verifying if any pixel's attention weight exceeds given threshold т. Second, Pglobal 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 follows:
TT (ag) st)
(()) = f(attn, gt bhox T) x Ee
Tota Tota (t)) A5 + y KL[Taaa(-|sa ne x-((ss)))
(7)
where Te(aa)s)) means the current policy, (ala(a means the old policy. The advantage function A₁ measures the relative value an action, guiding the optimization process. Additionally, the hyperparameter controls the influence of the KL divergence term, (L(Tauu-lssa) (-(()s))], 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 QwenV1.2.593B/7B [35] model as the base model and train epochs. The a, 8, % T used in the formula are set 1, 2, 0.004, and 0.2, respectively, During inference, to ensure fairness, we apply a unified and simple prompt across all benchmarks under zero- shot configurations. All experiments
conducted using NVNIIDI A 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-Pre [4], AndroidControl-Lonn [19], AndroidControl- High [19] and OmniAct(20J. Following UGround [6], we use two commonly adopted metrics for GUT 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 step 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 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-Prt focuses on high- resolution 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-72BB 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-Vi LL- 7B and the SFT-trained Qwen2.5-VL-7B models using the same dataset, the RFT-based SE- BE-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-constrainee environments.

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Table 1: Performance comparison of different agent models across various task categories based on Text, Icon, and Average scores on ScreenSpot-Pro Results marked in bold represent the best performance, and those underlined indicate the second-best performance.
CAD
Dev
Creative
Scientific
Office
OS
Avg.
Model
Text Icon Text Icon Text Icon Text Icon
Text Icon
Text Icon
Text Icon Avg.
Proprietary Models
GPT-4o [36]
2.0
0.0
1.3
0.0
1.0
0.0
2.1
0.0
1.1
0.0
0.0
0.0
1.3
0.0
0.8
Claude Computer Use [37]
14.5
3.7
22.0
3,9
25.9
3.4
33.9
15.8
30.1
16.3
11.0
4.5
23.4
7.1
17.1
General Open-source Models
Qwen2.5-VI.-3B [22]
9.1
7.3
22.1
1.4
26.8
2.1
38.2
7.3
33.9
15.1
10.3
23.6
3.8
16.1
Qwen2.5-VI. VT.-7B [22]
16.8
1.6
46.8
4.1
35.9
7.7
49.3
7.3
52.5
20.8
37.4
38.9
7.1
26.8
GUI GUL-specific Models
CogAgent-1883 [1]
7.1
3.1
14.9
0.7
9.6
0.0
22.2
1.8
13.0
0.0
5.6
0.0
12.0
0.8
7.7
Aria-U1
7.6
1.6
16.2
0.0
23.7
2.1
27.1
6.4
20.3
1.9
4.7
0.0
17.1
2.0
11.3
OS-Atlas-7B [38]
12.2
4.7
33.1
1.4
28.8
2.8
37.5
7.3
33.9
5.7
27.1
4.5
28.1
4.0
18.9
ShowU1-2B [33]
2.5
0.0
16.9
1.4
9.1
0.0
13.2
7.3
15.3
7.5
10.3
10.8
2.6
UGround- 7B [39]
14.2
1.6
26.6
2.1
27.3
2.8
31.9
2.7
31.6
11.3
17.8
0.0
25.0
2.8
16.5
UGround-V1-s -V--BBB [39]
15.8
1.2
51.9
2.8
47.5
9.7
57.6
14.5
60.5
13.2
38.3
7.9
45.2
8.1
31.1
UI-R1-3B [40]
11.2
6.3
22.7
4.1
27.3
3.5
42.4
11.8
32.2
11.3
13.1
4.5
24.9
6.4
17.8
GUI-RI-3B [10]
26.4
7.8
33.8
4.8
40.9
5.6
61.8
17.3
53.6
17.0
28.1
5.6
GUI-RI-7B [10]
49.4
38.9
55.6
11.8
26.4
42.1
16.9
UI-TARS-28 [2]
17.8
4.7
47.4
4.1
42.9
6.3
56.9
17.3
50.3
17.0
21.5
5.6
39.6
8,4
27.7
UI-TARS-7B [2]
20.8
9.4
58.4
12.4
50.0
9.1
63.9
31.8
63.3
20.8
30.8
16.9
47.8
16.2
35.7
UI-TARS-72B
18.8
12.5
62.9
17.2
57.1
15.4
64.6
20.9
63.3
26.4
42.1
15.7
50.9
17.6
38.1
Ours
SE-GUI-3B
38.1 12.5
55.8
7.6
47.0
4.9
61.8
16.4
59.9
24.5
40.2
12.4
50.4
11.8 35.9
SE-GUI-7B
51.3
42.2 68.2
19.3
57.6
9.1
75.0
28.2
78.5
43.4
49.5
25.8 63.5 21.0 47.3
Table Step accuracy on AndroidControl over 500 random actions from the test split. Baseline results are from [19]. Note that previous work trained AndroidControl and GUIACT, while our methods the zero-shot setting are only trained on grounding data.
AndroidControl High
AndroidControll Low
OmniACT
Planner
Grounding
Grounding
SR
Grounding
SR
AS
Supervised Setting
GPT-4o SeeClick [18]
39.4
47.2
29.6
GPT-4o
UGroundV1-78 [6]
48.4
62.4
32.8
Zero-shot Setting
GPT-4o
Qwen2.5-VL-7B (35]
31.6
36.0
62.6
58.0
24.1
GPT-4o
SE-GUI-7B
65.6
52.8
79.6
68.2
34.4
Agent evaluation. We evaluate our method, SE-GUI, on three challenging GUT agent benchmarks: Sovroiddonttoo_Hiige 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 ly 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 step 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 fine- grained instruction, SE-GUI achieves 52.8% accuracy, outperforming GPT-4o (39.4%) and UGround-V V1 (48.4%). In the high-level setting, where only the task goal is given, SE-GUT achieves 68.2%, +5.8% improvement over the strongest baseline, UGround-Vi (62.4%). Notably, unlike prior methods such as UGround-V 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.
7

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Table Performances on various platforms (Mobile, Desktop, Web) on ScreenSpot and ScreenSpot- v2. All experiments were conducted using raw screenshot information. Results marked in bold represent the best performance, and those underlined indicate the second-best performance.
ScreenSpot Accuracy (%)
ScreenSpot-v2 Accuracy (%)
Model
Mobile
Desktop
Web
Avg.
Mobile
Desktop
Web
Avg.
Proprietary Models
GPT-4o [41]
21.9
17.8
9.4
18.8
22.5
22.2
12.4
20.1
General Open-source Models
Qwen2-VLL VL-7B [22]
50.3
40.4
27.4
42.9
39.4
50.1
27.7
39.8
OW [22]
55.5
55.5
44.0
39.1
46.9
Qwen2.5-VI--7B [22]
84.7
92.8
78.4
85.4
86.5
GUI-specific Models
CogAgent-18BD [26]
57.8
31.6
40.1
47.4
50.6
51.6
54.1
52.8
SecClick-7B [18]
68.1
48.8
41.8
53.4
51.8
65.5
40.7
53.9
OSAtlas-4B [7]
56.2
74.9
69.9
68.5
74.9
56.9
70.0
68.5
UGround-7B [6]
75.9
75.8
78.3
73.3
74.3
74,9
78.6
76.3
Show [33]
84.8
70.8
76.2
75.1
70.0
85.1
73.3
77.3
OSAtlas-BB [7]
85.0
78.8
84.5
82.5
78.3
85.5
83.8
83.3
Aguvis-7B [8]
86.9
82.4
84.7
84.4
89.6
86.8
84.9
87.3
UI-TARS-2B [2]
85.0
81.4
79.8
82.3
87.9
81.4
82.9
84.7
Ours
SE-GUI-7B
85.6
91.4
86.5
88.2
95.2
87.1
87.0
90.3
For desktop and web environments, we evaluate on the OmniACT dataset [42], which comprises 9,802 tasks spanning 38 applications and 27 websites 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 HLLM-bbsee grounding system. Specifically, we prompt an MI.LM to generate textual descriptions of target UI elements, then 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-Vi (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.
SE-GUT predict their screen coordinates. These coordinates composed into
4.3 Ablation Study
Image resolution and data quality. To investigate the impact of image resolution and data quality on GUI RFT, conduct corresponding ablation experiments, with the results shown in Fig. 4(a) and Tab. When training on a filtered, higher quality version 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 million to 5 million. As the resolution increases, the model's ability recognize small but critical UT elements can be improved, leading 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 noticeable impact on the final performance. Specifically, setting the ratio of format reward to point reward 1:1 leads to slight performance drop. To address this, adjusted the weight ratio to 1:2 during training. We attribute this to the relative simplicity of

---

Table Ablations for SE-GUI-75 on ScreenSpot- The left table ablates the reward weights and 7, while the right table ablates т.
Reward Weight (a : (5)
7
Average
T
Average
1:1
1:2
0.04
0.004
0.4
0.3
0.2
0.1
X
24.85
42.95
X
27.07
43.87
x
/
31.31
J
X
44.78
X
/
X
/
34.28
X
X
X
43.51
(a) Pin Jack's conversation.
(b) Initiate a video call with Ash.
Figure 3: Visualization of the model's spatial attention on different samples.
learning format consistency; reducing 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 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 the attention map filtering threshold. When the threshold set too high, it may filter out samples with potential for reasoning. Conversely, when the threshold is too low, it fails provide effective supervisory guidance. Therefore, we selected 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, rewards, dense rewards bring a 4.21% improvement. This because dense rewards allow the model to receive more reward signals during the early stages of training, thus guiding the model focus the correct positions of UI elements.
shown in Table 5. The results indicate that, compared to sparse
The effectiveness of seff-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 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 '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 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

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Table 5: Ablations for data quality and reward function on ScreenSpot-
Data quality
reward function
CAD
Dev
Creative
Scientific
Office
OS
Avg.
original
w/iltering
sparse
dense
x
X
14.6
26.4
28.6
40.1
42.6
14.3
27.1
x
X
/
20.3
30.8
26.1
38.2
54.3
25.0
31.3
V
/
x
14.8
31.1
35,8
50.0
51.3
25.5
35.8
X
36.4
34.5
32.3
47.6
57.8
29.1
40.0
stage's reward curve closely aligns with that of the preceding stage, indicating that the model has almost converged.
In addition, shown in Fig. 4(d), observe that the ScreenSpot- Pro dataset, the previous state-of-the-ee model, UI-TARS-72B, performs poorly certain specialized scenarios, with accuracy dropping below 10%. In contrast, ing significant improvements these challenging settings. Specifically, it boosts accuracy by nearly 40% SolidWorks (SW) and Inventor (INV), which validates the effectiveness of our approach.
model demonstrates strong generalization capabilities, achiev-
ScreenSpot tho Accuney (%)
1544
ScreenSpot sroo Accuncy (N)
LI-TARS-225
SE-OU1176
56.5
46.3
Response Reward
(50
18
15
>
Sugr
16
212
Sugs
Xuge
piscli
Training Stage
Training Itenation
2k
2k
Proficssional Environments
CAB
SW IWV
(c)
(d)
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-725 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 these visual tokens. The attention values corresponding to the visual tokens are then mapped back to the original image resolution, yielding 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 '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 the right fails to localize the ground truth region, likely due to its inability to recognize the corresponding "video call" icon.
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 RI.-based methods, we employ data filtering strategy to curate high-quality dataset, on which we train a base model. Building upon this, adopt a self-evolutionary reinforcement fine-tuning paradigm to progressively enhance model performance. Our approach achieves state-of-the-ar 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 hardware limitations, this paper explores only two model scales: 3B and 7B. For the 7B model, we further constrained the maximum input resolution to million pixels to limit the number of visual tokens, which may result 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 72B.
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assets are released, the license, copyright information, and terms use in the package should be provided. For popular datasets, paperswi thcode com comddatasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
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16. Declaration of LLM usage
Does the paper describe the usage of LLMs if 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, originality of the research, declaration is not required.
Question:
Answer: [Yes]
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We adopt Qwen2.5-vl as the base model in constructing our SE-GUI.
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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 alternative commonly used Proximal Policy Optimization (PPO) [12] by eliminating the need for a critic model. Instead, GRPO directly compares group of candidate responses determine their relative quality.
In GRPO, given a task question, the model generates set of N potential responses for ...... ON J. Each response is evaluated by taking the corresponding actions and computing its reward (ri, r.,..., rN }. Unlike PPO, which relies on a single reward signal and critic to estimate the value function, GRPO normalizes these rewards to calculate the relative advantage of each response. The relative quality A; of the i-th response is computed as
A Mean((ra (r.,
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 better capture nuanced differences between candidates. Policy updates further con- strained by minimizing the KL divergence between the updated and reference models, ensuring stable RL learning.
B
More training details.
Due 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 screenshot to determine whether contains 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 the image contains a single, valid, and complete 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)
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Training Prompt.
You are helpful assistant. #Tools
You may call one or more functions assist with the user query.
You provided with function signatures within <tools></tools> XML tags:
<tools> t"type" "function", "function":
cimme_for_human "computer_use" "name": "computer_use" "description"; "Use a mouse and keyboard to interact with computer, and take screenshots. This is an interface to a desktop GUI. You do not have access menu. You must click on desktop icons to start applications. Some applications may take time start or process actions, so you may need to wait and take successive screenshots your actions. The screen's 's resolution is 1876x1036.
terminal
applications
see the results of
Whenever you intend 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 json object with function name and arguments within <tool_
move the cursor to click on
XML tags:
<tool_call> {"name" <function name>> "arguments": <args - jaon - object >} </tool_call>
train/rewards/pointeewwddd
1.5
1
0.5
train train/global_step
1k
2k
3k
Figure 5: Training curve of our SE-GUI-7B model.
22


Reviews:
[{"review_id": "464rUX3hao", "reviewer": "Reviewer_z7zC", "summary": "The 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.", "questions": "See weaknesses above.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "__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.", "was_revised": true, "final_justification": "I would have liked to see a stronger baseline for the \"Self-Evolutionary Reinforcement Fine-Tuning\" method, but the other strengths have me leaning towards acceptance.", "rebuttal": "We sincerely thank reviewer z7zC for the acknowledgment of our strong performance on competitive benchmarks and the recognition that our SE-GUI-3k could benefit the broader community. Below, we provide detailed responses to each concern.\n\n>W1. The attention-based approach is less intuitively compelling than other aspects of the method.\n\nOur attention-based method provides unique advantages: We deconstruct the grounding task into two fundamental stages: comprehension (interpreting the visual input and user query) and localization (outputting precise coordinates). We argue that most existing models are bottlenecked by accurate localization. Frequently, a model demonstrates a clear understanding of the target object yet errs in its reported coordinates. This discrepancy is empirically supported by experiments like the ground-truth vs. prediction analysis in SeeClick(ACL 2024) (many incorrect predictions are also close to the answer, suggesting the model recognizes the target but needs improvement in fine-grained localization). Consequently, attention mechanisms offer a direct window into the model's internal focus. They allow us to verify whether the model has successfully attended to the correct target, even when the subsequent localization step is erroneous.\n\nIn contrast, conventional threshold-based filtering is suboptimal as it cannot distinguish between two distinct types of low-scoring samples: **(a) roughly identifies the correct region but fails precise localization vs. (b) completely misses the target area**. A fixed threshold would indiscriminately discard both. Our attention-based approach, however, is designed to address this limitation. It selectively retains these valuable samples, allowing the model to gradually learn to refine its predictions on these challenging yet informative examples as training progresses. Since most current models use Transformer architectures, our method has broad applicability without requiring architectural modifications.\n\n> W2. Should the baseline be trained for the same total number of steps as all stages combined?\n\nWe understand your concern on the fairness of the ablations studies. We ensured all ablation trainings converged. Additionally, we found that the number of steps showed weak influence on model performance, as we will demonstrate below. We conducted experiments training baseline models for equivalent total steps (40 epochs), as shown in the table below. \n\n| Training epoches   | ScreenSpot-Pro Performance |\n|:------------------:|:--------------------------:|\n| 10                 | 39.9%                      |\n| 20                 | 40.3%                      |\n| 30                 | 39.4%                      |\n| 40                 | 38.9%                      |\n| iterative training | 47.3%                      |\n\nResults demonstrate minimal improvement with prolonged training, eventually suffering from performance degradation due to overfitting. This confirms that our gains come from iterative attention-guided data curation rather than simply longer training duration. The key insight is that our method changes what the model learns from, not just how long it learns. It shows an important distinction between our multi-stage approach and extended single-stage training. Our method is fundamentally different from simply training longer—each stage uses attention analysis to curate a different subset of training data rather than continuing training on the same data. The Stage 1 model's attention maps guide Stage 2's data selection, creating an evolving curriculum that adapts based on the model's demonstrated focus patterns.\n\n> N1. Regarding concurrent work\n\nWhile the ScreenSpot-Pro leaderboard shows some models with stronger performance, such as UI-TARS-1.5 and GTA. However, UI-TARS-1.5 is trained on private data, with their 7B model achieving 49.6\\% (marginally better than our 47.3\\%). GTA-1-7B reports 50.1\\% accuracy but is trained on million-scale datasets. In contrast, we achieve competitive results using only 3k open-source data points, demonstrating superior data efficiency while maintaining comparable performance."}, {"review_id": "dVV9rICNfY", "reviewer": "Reviewer_g7ps", "summary": "This 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.", "questions": "**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.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "This paper leverages dense point reward and attention-map-based training sample filtering for RL training in GUI grounding tasks and achieves impressive performance boost with smaller model and less training corpora. I don't see significant weaknesses. The missed result analyses and comparisons are supplemented during rebuttal. The discovered ambiguities are handled, too.", "rebuttal": "We sincerely thank reviewer g7ps for recognizing the novelty of our work, particularly the exploration of attention maps to filter training samples. We are encouraged by the recognition of our impressive performance boost. Below, we address the specific questions raised.\n\n> Q1. Why use attention-map score as a constant coefficient rather than a component in the composite reward?\n\nWe designed attention as a gating mechanism rather than a reward component for a fundamental reason: **data quality control vs. gradient weighting**.  As a **gating mechanism**: Poor-attention samples are completely filtered out, preventing the model from learning from potentially misleading supervision signals. As a **reward component**: $R = \\alpha R_f + \\beta R_p + \\gamma R_{att}$ would still allow poor-attention samples to contribute to training with lower weights, potentially introducing noise. Integrating attention scores into the reward signal does not significantly alter the learning process. This is because samples with low attention values are typically the ones the model fails to predict correctly anyway, resulting in an inherently low task reward to begin with. Our goal is **selective learning** on high-quality samples rather than **weighted learning** on all samples. In GUI grounding, where spatial attention alignment is crucial, we found that complete filtering yields better results.\n\n> Q2.1. Performance Analysis Questions\n\nWe investigated these phenomenons:\n\n1. **Is there some special difficulties for Creative scenarios?**: Creative categories (Photoshop, Blender, AI, etc.) involve specialized professional tools with: **non-standard UI elements and custom icons**, **high-resolution interfaces requiring fine-grained localization** and **domain-specific visual vocabularies underrepresented in pre-training data**. This creates a fundamental knowledge gap that RL cannot bridge-it optimizes existing capabilities rather than acquiring new domain expertise.\n\n2. **The performances on Creative and Scientific categories drop when using dense reward both with and without data filtering:**: While dense rewards generally provide more granular feedback, reinforcement learning training is inherently stochastic. We conducted two separate runs of the initial training phase and observed that the overall performance remained stable at approximately 40.0, although there were slight fluctuations in performance across different sub-categories between the runs.:\n\n|        | Creative | Scientific | Overall |\n|:-------|:--------:|:----------:|:-------:|\n| Run #1 |   35.8   |    50.0    |  40.0   |\n| Run #2 |   34.0   |    51.0    |  40.4   |\n\n3. **The performance of icon grounding on Creative category is remarkably lower:**: Icon recognition heavily depends on visual concepts learned during pre-training. Creative software icons are significantly underrepresented in typical training corpora, explaining why *all models* (including UI-TARS-7B 9.1\\% accuracy on creative icon grounding) consistently struggle in these categories—this is a dataset limitation, not a method-specific issue.\n\n> Q2.2. Missing Results\n\nThanks for the comment. We provide the missing UI-TARS-7B/72B and SE-GUI-3B results below:\n\n\n**ScreenSpot**\n| Model       | Mobile | Desktop | Web  | Avg      |\n|:------------|:------:|:-------:|:----:|:--------:|\n| UI-TARS-7B  | 94.5   | 85.2    | 95.9 | 89.5     |\n| UI-TARS-72B | 94.9   | 82.5    | 89.7 | **88.4** |\n| SE-GUI-3B   | 92.4   | 83.2    | 83.7 | 87.4     |\n| SE-GUI-7B   | 85.6   | 91.4    | 86.5 | **88.2** |\n\n**ScreenSpot-v2**\n| Model       | Mobile | Desktop | Web  | Avg      |\n|:------------|:------:|:-------:|:----:|:--------:|\n| UI-TARS-7B  | 96.9   | 89.1    | 95.4 | 91.6     |\n| UI-TARS-72B | 94.8   | 86.3    | 91.2 | **90.3** |\n| SE-GUI-3B   | 95.6   | 85.5    | 86.5 | 89.3     |\n| SE-GUI-7B   | 95.2   | 87.1    | 87.0 | **90.3** |\n\n**ScreenSpot-Pro**\n| Model       | Avg      |\n|:------------|:--------:|\n| UI-TARS-7B  | 35.7     |\n| UI-TARS-72B | **38.1** |\n| SE-GUI-7B   | **47.3** |\n\nAlthough UI-TARS-7B outperforms our SE-GUI-7B on ScreenSpot (89.5\\% vs. 88.2\\%) and ScreenSpot-v2 (91.6\\% vs. 90.3\\%), **SE-GUI-7B matches the performance of the much larger UI-TARS-72B on ScreenSpot (88.2\\% vs. 88.4\\%) and ScreenSpot-v2 (90.3\\% vs. 90.3\\%)**. We argue that performance on these two datasets appears to be approaching saturation. Additionlly, UI-TARS benefits from training on 50 billion tokens including an undisclosed amount of proprietary annotated data. In contrast, our model is trained on only 3K curated samples. We demonstrate that with strategic data curation and iterative refinement, it is possible to achieve competitive performance using orders of magnitude less data.\n\nFurthermore, UI-TARS requires extensive data collection and computational resources, which may not be accessible to most researchers. Our approach, relying solely on open-source data, achieves comparable results with significantly lower resource demands, further validating the effectiveness of our data-efficient methodology.\n\nWe deliberately focus on ScreenSpot-Pro, as its increased difficulty offers **a more discriminative evaluation of model capabilities**. The substantial performance gap observed on this benchmark (**47.3\\% vs. 38.1\\%** against UI-TARS-72B) highlights the clear advantage of our data-efficient approach in scenarios with limited supervision and higher task complexity.\n\n>Q3. Clarity issues\n\nThanks for the detailed suggestion. The reward $r_i$ represents the sum of format reward and point reward: $r_i = \\alpha R_f + \\beta R_p$. \"input text for scroll actions\" should be \"input text for type actions\". We will revise the manuscript accordingly."}, {"review_id": "6Hcaogni7M", "reviewer": "Reviewer_iFgo", "summary": "This 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.", "questions": "Following 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.", "limitations": "A limitation of this method is the lack of evaluation with scalability to larger models, which is briefly discussed by the authors in Section 5.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The issues about 1) basic ablation and 2) selective reporting are resolved. \nThe issue of lack of visual illustration is remained unsolved, since the authors were not able to attach images in the rebuttal. \nThe authors are expected to incorporate detailed visual examples in the final version to enhance the clarity and impact of their results.", "rebuttal": "We sincerely thank reviewer iFgo for the acknowledgment that our high-quality filtered dataset could benefit the research community and that our task-specific method demonstrates reasonable design with significant performance gains on ScreenSpot-Pro. Below, we provide detailed responses to each concern.\n\n> Q1 & W1.Missing Ablation on Core Term\n\nThis ablation is embedded within our experimental design and results are reported in Figure 3(b). Our multi-stage training provides a natural ablation study:\n\n- **Stage 1**: Standard GRPO without attention filtering ($f(\\cdot) = 1$ for all samples)\n- **Stage 2+**: GRPO with attention-based sample filtering ($f(\\cdot)$ varies based on attention quality)\n\nAs shown in Figure 3(b), Stage 1 achieves 39.97\\% while Stage 2 reaches 43.0\\%, directly demonstrating the 3.03\\% improvement from attention-based filtering. This comparison isolates the effect of our core contribution while controlling for all other factors.\n\nAdditionally, we provide threshold sensitivity analysis ($\\tau \\in \\{0.1, 0.2, 0.4\\}$) in Appendix D, showing that $\\tau=0.2$ yields optimal filtering performance.\n\n> Q2 & W2. Lack of Visual Evidence\n\nThanks for your suggestion. Additional figures are not permitted in the rebuttal, and we will include visualization results in the revision. However, based on our current analysis, we have observed that state-of-the-art (SOTA) methods often struggle in two key categories of scenarios. The first involves identifying UI icons and accurately grounding them to specific locations, such as in tasks like “close the location labels on the map” or “delete the 'article' tag for searching.” The second involves professional or domain-specific software operations, such as “use the select tool” or “adjust the style.”\n\nOur method consistently performs better in these challenging scenarios, thanks to its stronger generalization capabilities and the use of an attention-based alignment mechanism that jointly models semantic intent and visual grounding. These advantages are also reflected in our results on the ScreenSpot-Pro benchmark, which is specifically designed to test such difficult cases.\n\nOn ScreenSpot-Pro, our model demonstrates significantly stronger icon grounding performance compared to SOTA baselines. Furthermore, for professional software-related queries, the accuracy of UI-TARS-72B drops below 10\\%, while our model achieves up to 40\\%, underscoring the robustness and practical utility of our approach in real-world, specialized UI tasks.\n\n> Q3 & W3. Selective Performance Reporting\n\nThanks for the comment. We provide the missing UI-TARS results below:\n\n\n**ScreenSpot**\n| Model       | Mobile | Desktop | Web  | Avg    |\n|:------------|:------:|:-------:|:----:|:------:|\n| UI-TARS-7B  | 94.5   | 85.2    | 95.9 | 89.5   |\n| UI-TARS-72B | 94.9   | 82.5    | 89.7 | **88.4** |\n| SE-GUI-7B   | 85.6   | 91.4    | 86.5 | **88.2** |\n\n**ScreenSpot-v2**\n| Model       | Mobile | Desktop | Web  | Avg    |\n|:------------|:------:|:-------:|:----:|:------:|\n| UI-TARS-7B  | 96.9   | 89.1    | 95.4 | 91.6   |\n| UI-TARS-72B | 94.8   | 86.3    | 91.2 | **90.3** |\n| SE-GUI-7B   | 95.2   | 87.1    | 87.0 | **90.3** |\n\n**ScreenSpot-Pro**\n| Model       | Avg      |\n|:------------|:--------:|\n| UI-TARS-7B  | 35.7     |\n| UI-TARS-72B | **38.1** |\n| SE-GUI-7B   | **47.3** |\n\nAlthough UI-TARS-7B outperforms our SE-GUI-7B on ScreenSpot (89.5\\% vs. 88.2\\%) and ScreenSpot-v2 (91.6\\% vs. 90.3\\%), **SE-GUI-7B matches the performance of the much larger UI-TARS-72B on ScreenSpot (88.2\\% vs. 88.4\\%) and ScreenSpot-v2 (90.3\\% vs. 90.3\\%)**. We argue that performance on these two datasets appears to be approaching saturation. Additionlly, UI-TARS benefits from training on 50 billion tokens including an undisclosed amount of proprietary annotated data. In contrast, our model is trained on only 3K curated samples. We demonstrate that with strategic data curation and iterative refinement, it is possible to achieve competitive performance using orders of magnitude less data.\n\nFurthermore, UI-TARS requires extensive data collection and computational resources, which may not be accessible to most researchers. Our approach, relying solely on open-source data, achieves comparable results with significantly lower resource demands, further validating the effectiveness of our data-efficient methodology.\n\nWe deliberately focus on ScreenSpot-Pro, as its increased difficulty offers **a more discriminative evaluation of model capabilities**. The substantial performance gap observed on this benchmark (**47.3\\% vs. 38.1\\%** against UI-TARS-72B) highlights the clear advantage of our data-efficient approach in scenarios with limited supervision and higher task complexity.\n\n> Q4 & W4. Clarity, figure and type issues\n\nThanks for the detailed suggestion. We will clarify the $R_{\\text{point}}$ notation, properly reference Appendix Figure 6 in the main text and improve quality of some figures. We will revise the manuscript accordingly."}, {"review_id": "k6DVZj2eCn", "reviewer": "Reviewer_AHhf", "summary": "This 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.", "questions": "1.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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\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？", "was_revised": true, "final_justification": "The rebuttal satisfactorily addressed my concerns on data filtering reliability, scalability, and data scaling trends. While large-scale (32B/72B) experiments remain untested, the provided evidence from 3B→7B scaling and diversity-focused data analysis is convincing. I maintain my borderline accept recommendation, noting that further large-scale evaluations would strengthen the work.", "rebuttal": "We sincerely thank Reviewer AHhf for recognizing the novelty of our work, particularly appreciating our innovative approach of self-evolutionary reinforcement fine-tuning and impressive performance on multiple benchmarks. Below, we provide detailed responses to each concern.\n\n> W1 & Q1. Data Filtering Reliability Concerns\n\nThanks for the comment. Difficulty is a relative measure that depends on the model. We apply data filtering using the specific model targeted for training to support our adaptive curriculum design as already-mastered samples provide no learning signal (zero gradient) during RL.\n\nAdditionally, we want to emphasize that our filtering process is not exclusively based on Qwen2.5-VL-7B.  As described in Section 3.1, for Instruction Quality Filtering and Bounding Box Quality Filtering, we use GPT-4 and a fine-tuned variant of Qwen2.5-VL-7B, as the criteria for distinguishing good from poor examples are well-defined.\n\n> Q2. Scaling to Larger Models\n\nThanks for the comment. Unfortunately, due to constraints in both computational resources and time, we could not afford RL on a 32B/72B model. Nevertheless, our experiments demonstrate successful scaling from 3B to 7B models with consistent improvements, while concurrent works such as UI-R1 and GUI-R1 limited their experiments to 3B models.\n\nFrom a theoretical perspective, larger models typically exhibit better attention quality and spatial reasoning capabilities, which should enhance our filtering effectiveness as attention maps become more precise with increased model capacity. Our core contributions—attention-based filtering and iterative data curation—operate at the architectural level through transformer attention mechanisms rather than model-specific features, suggesting natural scalability as model size increases.\n\n>Q3. Data Scaling\n\nThanks for your comment. We have systematically studied data scaling and observed encouraging trends. As shown below, increasing the number of high-quality filtered samples leads to steady improvements on ScreenSpot-Pro:\n\n| Filtered Data Size | ScreenSpot-Pro Performance |\n|:------------------:|:--------------------------:|\n| 485                | 42.3%                      |\n| 972                | 43.0%                      |\n| 1,700              | 44.8%                      |\n| 3,018              | 47.3%                      |\n| 4,000              | 48.0%                      |\n\nMoreover, our filtering strategy naturally balances data quality and quantity. Unlike traditional approaches that scale linearly with raw data volume, our method emphasizes useful data density—each filtered sample carries a higher learning signal. Through additional analysis, we find that data diversity plays a more critical role than sheer volume: adding 1K diverse samples leads to greater performance gains. We believe that incorporating more high-quality, diverse filtered samples has the potential to further enhance performance."}]

Author rebuttal:

Meta-review: The consensus among the reviewers, following a thorough discussion period, is positive, with all reviewers leaning toward acceptance after their initial concerns were effectively addressed by the authors.

The reviewers found a few strengths of the paper. 
- The paper's most significant achievement is demonstrating that its 7B parameter model, trained on a curated dataset of only 3,000 samples, can outperform a much larger 72B parameter model on the challenging ScreenSpot-Pro benchmark. This result is a strong statement on the value of data quality and intelligent training methods over sheer scale, a point consistently praised by all reviewers .
- The work introduces a novel three-part framework that is well-motivated and effective. The combination of careful seed data curation, a dense point reward mechanism to overcome sparse feedback , and a self-improvement fine-tuning loop using attention maps is seen as a reasonable design. 
- The reviewers see the curated 3k-sample dataset a valuable asset that could benefit the broader research community by providing a high-quality benchmark for training and evaluation.

Thanks the authors for providing detailed responses to the reviewers' questions. They successfully clarified the rationale behind their attention-based filtering, provided missing experimental comparisons, and addressed concerns about the fairness of their ablations and the reliability of their data curation process.

The reviewers also pointed out a few weaknesses of the current manuscript.
- First of all, the approach was validated on 3B and 7B models, but its applicability to much larger models (e.g., 32B or 72B) remains an open question due to computational constraints. While the authors provide a reasonable argument for why it should scale, empirical evidence is missing. This was noted as a limitation but not a fatal flaw.
- The initial submission lacked visual examples illustrating cases where SE-GUI succeeds and prior methods fail. The authors have committed to adding these to the final version, which will be important for a more intuitive understanding of the method's advantages.
- The model's performance sees a notable drop in specialized "Creative" and "Scientific" software environments.

Overall, the paper seems to make a solid contribution.
Decision: Accept (poster)