WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning
Authors: Zimu Lu
Houxing Ren
Yunqiao Yang
Ke Wang
Zhuofan Zong
Junting Pan
Mingjie Zhan
Hongsheng Li
Venue: ICLR 2026 Poster (2026)
Keywords: code agent
website generation
large language model
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose WebGen-Agent, a novel website-generation agent that leverages comprehensive and multi-level visual feedback to iteratively generate and refine the website codebase.

Abstract: Agent systems powered by large language models (LLMs) have demonstrated impressive performance on repository-level code-generation tasks. However, for tasks such as website codebase generation, which depend heavily on visual effects and user-interaction feedback, current code agents rely only on simple code execution for feedback and verification. This approach fails to capture the actual quality of the generated code. In this paper, we propose WebGen-Agent, a novel website-generation agent that leverages comprehensive and multi-level visual feedback to iteratively generate and refine the website codebase. Detailed and expressive text descriptions and suggestions regarding the screenshots and GUI-agent testing of the websites are generated by a visual language model (VLM), together with scores that quantify their quality. The screenshot and GUI-agent scores are further integrated with a backtracking and select-best mechanism, enhancing the performance of the agent. Utilizing the accurate visual scores inherent in the WebGen-Agent workflow, we further introduce Step-GRPO with Screenshot and GUI-Agent Feedback to improve the ability of LLMs to act as the agent-engine model. By using the screenshot and GUI-agent scores at each step as the reward in Step-GRPO, we provide a dense and reliable process supervision signal, which effectively improves the model's website-generation ability. On the WebGen-Bench dataset, WebGen-Agent increases the accuracy of Claude 3.5 Sonnet from 26.4\% to 51.9\% and its appearance score from 3.0 to 3.9, outperforming the previous state-of-the-art agent system. Additionally, our training approach increases the accuracy of Qwen2.5-Coder-7B-Instruct from 38.9\% to 45.4\% and raises the appearance score from 3.4 to 3.7.

Published as a conference paper at ICLR 2026
WEBGEN-AGENT: ENHANCING INTERACTIVE WEB- SITE GENERATION WITH MULTI-LEVEL FEEDBACK AND STEP-LEVEL REINFORCEMENT LEARNING
Zimu Lu Houxing Ren'*, Yunqiao Yang' Ke Wang", Zhuofan Zong', Junting Pan* Mingjie Zhan Hongsheng Li1.3,4,5t
Multimedia Laboratory (MMLab), The Chinese University of Hong Kong, Time, CPIII under InnoHK, *Shanghai Al Laboratory, *Ace Robotics
luz Auzimu@ Link. cuhk. edu. hk alanmingyie@ssesstt com hslißee. cuhk edu.hk .kk
ABSTRACT
Agent systems powered by large language models (LLMs) have demonstrated im- pressive performance on repository-level code-generation tasks. However, for tasks such as website codebase generation, which depend heavily on visual ef- fects and user-interaction feedback, current code agents rely only simple code execution for feedback and verification. This approach fails to capture the ac- tual quality of the generated code. In this paper, we propose WebGen-Agent, a novel website-geneee generation agent that leverages comprehensive and multi-level vi- sual feedback iteratively generate and refine the website codebase. Detailed and expressive text descriptions and suggestions regarding the screenshots and GUI- agent testing of the websites are generated by visual language model (VLM), together with scores that quantify their quality. The screenshot and GUI-agent scores are further integrated with a backtracking and select-best mechanism, en- hancing the performance of the agent. Utilizing the accurate visual scores inherent in the WebGen-Agenn workflow, we further introduce Peep-GRPO with Screenshot and GUl-agent Feedback to improve the ability of LLMs to act as the reasoning engine of WebCien-Agen By using the screenshot and GUI-agent scores at each step the reward in Step-GRPO, pervision signal, which effectively improves the model's website-generation abil- dataset, WebGen-Agen. increases the accuracy of
provide dense and reliable process
ity. On the WebGen-Bees Claude-3.5-Sonnet from 6..4% to 51.9% and its appearance score from 3.0 to 3.9, outperforming the previous of-te-of-theaar agent system. Additionally, our Step- GRPO training approach increases the accuracy of Bren2..-Coder--BB Instruct from 38.9% 45.4% and raises the appearance score from 3.4 to 3.7.
INTRODUCTION
Recent studies code agents have shown great advancements in repository-lewe code-generation tasks, such as fixing GitHub issues (Yang et al. 2024b) and implementing new features (Miserendino et al. 2025). However, for tasks like website code generation, which depend heavily visual aes- thetics and the fluency of user interactions, current code-agent systems fail to fully capture the actual quality of the generated codebase, because they mostly rely simple code-execution feed- back. This limitation can lead to various rendering and functional problems in the generated web applications, such misaligned components, disharmonious coloring, unresponsive buttons, and broken links.
To enable the code agent effectively handle such tasks, we introduce WebGen-Agent, code- generation system that generates websites from natural-language instructions that specify appear- ance and functional requirements, thus offering a highly automated -evelie-deeellmmennt process. To ensure that the generated websites meet both functional requirements and aesthetic standards, we
"quual contribution.
Corresponding author.

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Published as a conference paper at ICLR 2026
leverage both execution feedback and visual feedback to refine the project. Specifically, we lever- age a visual language model (VLM) to assess the visual appeal and aesthetic quality of the current website, and graphical user interface (GUT) agent to evaluate the correctness and intended fune- tionality of the website's 's codebase, thereby gathering accurate information and providing targeted suggestions. By iteratively applying this feedback and editing the codebase, WebGen-Agent builds websites with appealing designs and smooth interactive funccioonaityy.
As shown in Fig. D WebGen-Agen adopts an iterative, multi-step paradigm in which each step consists of three actions: code generation, code execution, and feedback gathering. The agent begins each step by creating and editing files in the codebase in manner similar to Bolt.diy (stackblitz labs 2024). During code execution, dependencies are installed, and the website service is started. If execution emits errors, the errors are returned to the agent, which starts the next step to fix them. five consecutive erroneous steps occur, the agent backtracks to previous non-erroneous step.
In the feedback gathering process, screenshot of the landing page of the website is captured first. A VLM then provides description and an appearance score based on the screenshot. If the screen- shot has room for improvement, the model supplies suggestions, which are implemented in the subsequent step to explicitly refine the website's visual aesthetics. Otherwise, a GUI-agent session is initiated to explore the website, which evaluates the functional requirements and generates cor- responding feedback. If the testing is successful, the task is complete; otherwise, suggestions for fixing the website are generated, and the agent can edit the codebase in the next step. At the end of the task trajectory, the best step is selected on the basis of the screenshot and GUI-agent scores, and the codebase is restored to the state of that step. Based on the pipeline, various models achieve better performance on WebGen-Bench (Lu al. 2025b), consistently outperforming other code agents. Remarkably, Claude-3.5-Somal improves its accuracy from 26.4% to 51.9% and its appearance score from 3.0 to 3.9, outperforming Bolt.diy.
To equip code agents with enhanced reasoning abilitics, we further propose Step-GRPO with Screenshot and GUI-agent Feedback. Given an instruction, multiple WebGen-Agent trajecto- ries are generated. Each step in an agent trajectory is accompanied by screenshot score and GUI-agent testing score, and an accurate and reliable step-level reward can computed by sum- ming these two scores. This dual supervision of website appearance and functionality effectively optimizes the model to generate high-quality website codebases, providing stepwise, process-level guidance for the agent trajectory, This process transforms information obtained from the VLM's 5---ooo---B--Bnnnn
visual ability into the coding LLM's programming skills. Training a model with this Step-GRPO approach increases the accuracy from 38.9% to 45.4% and raises the appearance score from 3.4 to 3.7 on WebGen-Bende greatly improving both the functionality and the appearance of the generated websites. We name the trained family of models WebGenAgent- LM.
Our contributions include:
We propose WebGen-Agent, a code-agent system that leverages screenshots and GUI-agent test- ing provide feedback signals and iteratively improve the quality of generated websites.
We introduce Step-GRPO with Screenshot and GUI-agent Feedback, which uses screenshots and GUI-agent scores as step-level supervision in the GRPO training process, significantly improving the performance of smaller open-source models.
Extensive experiments demonstrate the effectiveness of the proposed system. The system in- creases the accuracy of Claude-3.5-Somme 3.0 to 3.9, outperforming Bolt.diy. Our training approach also increases the accuracy of Qwen2.55 Coder-7B-Instruct from 38.9% to 45,4% and raises the appearance score from 3.4 to 3.7.
from 26.4% to 51.9% and its appearance score from
2 METHOD
In this section, we first introduce WebGen-Agent, a novel website generation system that leverages screenshots and GUI-agent testing as reliable feedback to iteratively refine both the appearance and functionalities of the generated website with a coding LLM. Building on the reliable visual scores produced by WebGen-Agent,
then propose Step-GRPO with Screenshot and GUI-agent Feedback, a method that uses these scores to provide process supervision during GRPO training. This system significantly enhances language models' ability to generate high-quality websites.
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Published as a conference paper at ICLR 2026
Screonshof Feeibbch Agent Testing Feetback
Feedback,
Code:
Error Code,
Code
Web Service
Comples
VIM
GUT Agent
Instruction
Reflaction with Execution Fasdbback
Iterative website generation with screenshot- and (Ull-aeet--iie feedback. A back- tracking and rest-step-selection mechanism applied on the basis of the screenshot and GUl-agent testing scores.
Figure 1:
2.1 WEBGEN-AGENT WORKFLOW
The WebGen-Agent workflow consists of multiple steps, with each step including code generation, code execution, and feedback gathering. As shown in Fig. [ the agent trajectory starts from a web- site generation instruction (I), denoted as T [Z]. and empty codebase Co. The instruction I is created by concatenating a system prompt similar to that of Bolt.diy (stackblitz labs, (2024) with the request. A coding LLM acting as the engine of the agent generates code AC₁ to edit the codebase, resulting in C1. Then, the dependencies of the codebase are installed, and the website service is started. The code execution output denoted as O1, which contains both stdout and stderr. If the dependency installation or service initialization fails, the output message Oj returned to the agent as feedback, so that the agent can fix the error in the next step. If no error occurs, screenshot of the website is captured and presented to a VLM (e.g.. Qwen2.5-VI. 32B; Bai et al. 2025)), which is requested to provide a description of the screenshot and, if needed, suggestions improve the website's 's appearance. The prompt for acquiring screenshot feedback is provided in Fig. Of Appendix A score of the website appearance based on the screenshot is also generated and, together with the description and suggestions, composes the screenshot feedback. The feedback can be denoted as:
user-provided
Sshot (Description, Scorestor Suggestions
Fahot is used to reflect the integrity and aesthetics of the website's appearance. Here, a separate VLM used besides the coding LLM make the system more cost-effective, we observe that a relatively small open-source VLM is sufficient for the task, while the code generation requires an LLM with strong coding abilities. We use -.vn2.5-VL-32B-BRE Instrrcct as the VLM in our ex- periments unless stated otherwise. Fahot focuses on page-level appearance, which is where VLMs currently demonstrate the highest reliability. Extending the task to multi-page inputs would intro- duce a qualitatively different problem formulation, as cross-page coherence is inherently subjective and highly context-dependent. The code exccution and screenshot feedback are appended to the agent trajectory, resulting in T [I, AC1, Fahot.l. Then, the agent judges whether the web- site's ' appearance is satisfactory based on the trajectory. If is unsatisfactory, the agent continues to generate code AC₂ to improve the website's appearance. Otherwise, the agent initiates GUI-agent testing session, generating an instruction for the GUI-agent to explore various website functional- ities specified in the instruction I, resulting in GUT-agent testing trajectory. The prompt used to generate the GUI-agent instructions is shown in Fig.66 Appendix [C It instructs the model to pro- duce GUI-agent instruction that comprehensively checks all website-development requirements and includes a one-shot example. As shown in Tab. of Appendix GJ manual inspection indicates that 98.3% of the sampled instructions achieve high coverage of the requirements. Based on the GUI-agent testing result, the L.LM acting as the engine of the agent judges whether the testing is successful and provides a score, denoted as Score The prompt for acquiring the GUT-agent test- ing feedback is provided in Fig. of Appendix C If the testing result is unsatisfactory, suggestions are also made to improve the functionality. Thus, the GUI-agent testing feedback can be denoted as:
F gui (Scoregui, Suggestions
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Published as a conference paper at ICLR 2026
Scrvenshut Fivdbark GUT Ageut Testing Foetback
Instruction
Codé;
Codez
Codéx
Step-GRPO
R
(1 (1)
(
Weh Servicey
17
mean
Servicez
Servicey
std(R)
Agent
Tounk
Figure 2: Step-GRPO with Screenshot and GUl-agent Feedback. Multiple WebGen-Ageen trajecto- ries are produced, and the reward for each step is computed by summing the screenshot score and the GUl-agent score.
Fgui is also appended to the trajectory, resulting in T = II, [I, Fshol1 F [I, AG, Fil Here, Fi denotes [Fshot F gggal. In this way, WebGen-Agemt continues to improve the appearance and functionality of the website, resulting in trajectory T, denoted as T [z. , AC2, F2, ACK, :x, Fk].
The process ends when the website passes the GUI-agent testing, or the maximum iteration number reached. During the iterations, at step i E {1,2, the codebase state C,, the edit AC,, together stored in memory list. If five consecutive steps contain code
with the Scoreshot, and Scoregui. execution errors, a backtracking mechanism triggered, and the agent trajectory and the codebase returned to the state at the best previous step. The best previous step is selected by first choosing the steps with the highest Scoregui- and then among these steps, the ones with the highest Score chosen. If there is still more than one chosen step, then the latest one among them is selected. Considering that later code edits might not always improve the previous codebase, at the end of the agent workflow, the best step among all the steps selected in the same way as mentioned above. A more detailed algorithmic presentation can be found in Appendix B and example trajectories are presented in Appendix
2.2 STEP-GRPO WITH SCREENSHOT AND GUI-AGENT FEEDBACK
While using strong proprictary models as the engine LLM WebGen-Agent can produce high performance, the agent workflow would be more cost-efficient if smaller open-source models of 7B- 8B parameters can be used instead. However, current small open-source language models still lag behind proprietary models in website code generation. Therefore, we introduce Step-GRPO with Screenshot and GUI-agent Feedback, leveraging the Score and Score inherently produced in the WebGen Wegntn-Agent workflow train them with step-level process supervision GRPO training.
Before the GRPO-based training, we first perform light supervised fine-tuning (SFT) using ap- proximately 700 WebGen-Agent trajectories generated by DeepSeck-V3, training for one epoch to serve warm start. Then, Step-GRPO is performed on the fine-tuned model. The Step-GRPO training objective can be written as:
(o) Pe (Ola))
G
lodl
Tolonalg, A clip (o 0i.
no(oualg, e, e.1+)) A.d].
(3)
min
Here, q denotes the website generation instruction, and {o,) denotes the group of trajec- tories generated from the instruction q. We remove the KL loss to encourage the model to more frrely adapt its behavior the reward signals QQian al. 2025). o, can be denoted as [AC1, A,, F1, ACK., Fk.l- Different from the naive GRPO, which sets the advantages on all tokens in trajectory the same value, the Step-GRPO sets advantages ferent steps to different values. In our work, the GRPO loss is only applied to the model outputs
tokens in dif-
AC1, AC ACx. We denote the reward of all tokens in the j-th step of ( as r (i) which computed by summing the Score and Scoregui of that step, generated in the WebGen-Agent workflow:
(3) Score shon.j r
Score (6) gui
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Published as a conference paper at ICLR 2026
The rewards for all steps in the trajectories sampled from 9 can be written R ((r) ... , -........ (ri (r16), .... ," () The advantage for step j of the i-th trajectory com- (8) m
puted by standardizing its immediate reward: 1
A(t) denotes the advantage
st)(R)
of ol the j-th step. We do not accumulate normalized rewards from future steps in Shao al. 2024). because in the website-generation task Score and Scoregui directly reflect the quality of the website at the current step, which is more appropriate for representing the desirability of the cur- rent code. The Step-GRPO training process is illustrated in Fig. 2 This Step-GPPO method, with screenshot and GUl-agent feedback, incorporates accurate step-level supervision and effectively helps the model learn to generate websites with an appealing appearance and smooth functionality.
3 EXPERIMENTS
In this section, we first present the performance of WehGien-Agent on WebGien- using a variety of proprietary and open-source LLMs, as well as models trained using Step-GRPO with Screenshot and GUI-agent Feedback. Then, we conduct comprehensive ablation studies on the design choices in the WebGen-Agent workflow and the Step-GRPO training process.
3.1 MAIN RESULTS
Benchmark Dataset and Baselines. We evaluate WebGen- Agent using WebGen-Bench (Lu et al. 20256), a benchmark containing 101 website-ged gesirrtignnrtioon instructions in natural language and 647 GUl-agent test cases, covering wide range of web applications. Following Lu et al. (2025b) we use Qwen2.5-VL-33B-Inste
(Bai et al. 2025) in functional testing and GPT-4o (Hurst et al.) 2024 in appearance evaluation. We compare WebGen-Agent with three other popular code agents: OpenHands (Wang et al. 2024). Aider (Aider-AI, 2024), and Bolt.diy (stackblitz labs) 2024). We present the results of OpenHands and Aider in combination with DeepSeek-V2 V3 (Liu et al.) (024), Claude-3.5-Somne (Anthropic 2024). and DeepSeek-RR (Guo et 20.520. as well as the results of Bolt.diy with DeepSeek- V3 (Liu al. 2024), Claude-3.5-Sonnet (Anthropic 2024). DeepSeek- RI (Guo et al. 2025a), GPT-4o (Hurst et al.J 2024), 03-mini (OpenAI) 2025b) Qwen2.5-Coder 32B (Hui al. 2024), Bwen2.5-77B-Instrrus
(Yang al. (024a),, WebGen-LM-
WebGen-LM- (2025b). We also
14B, and WebGen-LM-32B (Lu et al. 2025b). The values are taken from et al.
present results on the Web Application subset of the ArtifactsBench (Zhang al. Appendix|M M
2025a) dataset in
Models and WebGen-Agent Inference Settings. We evaluate WehGin-AAeet using a wide range of proprietary and open-source models as coding LLMs. The proprietary models we tested in- clude Claude-3.5-Sonne (Anthropic 2024), DeepSeek-RI (Guo et al. 2025a), DeepSeek-V3 (Liu
let al. 2024). o3 (OpenAI 2025a), Claude-4-Sonnet (Anthropic] (025). Gemini-2.5 (Comanici et al. 2025 and Qwen3 A-BB--333B-IInnnnn (Yang et al.) (025a). The smaller open-source
models tested include Owen2.5-Coder-333 -nsttuttt (Hui et al. (024).
Instruct (Yang et al. (025a), Qwen2.5-72B InItrrcct YYang et al. 2024a),, Qwen2.5-Codeer778 Instruct (Hui et al. (0024), and Qwen3-8B Yang et al. (025a)), well as 7B and 8B WebGen Agent- LM models trained with SFT and Step-GRPO. The maximum number of iterations is set to 20, and
the model temperature is set to 0.5. We use Qwwe2.5-VVL-32B-Inste Insttrcctt as the feedback VLM for screenshot and GUI-agent testing in all the experiments. Analysis of the maximum iteration number presented in Appendix
Training Settings. We first fine-tune Doven..-Codeer7BBBmaae mately seven hundred WebGen-Agent trajectories generated from website generation instructions taken from WebGen-Instruce (Lu et al. (025b) with DeepSeek-V V3 for one epoch with a learning rate of 4e-5 and a batch size 32. This results in the models NebGeenAgem-LLM-7BB-T BebGeenAgent-LM-SBB---
and Qwen3-8B on approxi-
and which serve as a warm start for Step-GRPO. We then train these SFT models using Step-GRPO on five hundred website generation instructions randomly sampled from WebGen- WebGen-Instruct and WebGenAgent-LM-BBB L-B-Ste--RRPO The learning rate is set to le-6 with a batch size of 16. For each instruction, we sample outputs. Ambiguous or underspecified instructions manually
Instruct for one epoch, resulting in the final models WebGenAgemt-LM.. 7B --e----ttpppCr Step-GRPO
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Published as a conference paper at ICLR 2026
Table 1: The performance of WebGen-Agene with various proprictary and open-source models on WebGen-Benee (Lu et al. 2025b), compared with other code-agent systems. Results of OpenHands, Aider, and Bolt.diy are taken from Lu et al. (2025b). Accuracy is computed using weighted score, where YES samples are weighted by and PARTIAL samples by 0.5; the total score is then divided by the number of test cases. The highest Accuracy Appearance Score are highlighted bold.
Test Name
Yes
Partial
No
Start Failed
Accuracy
Appearance Score
OpenHands
Claude-3.5-Sone 3.5-nonnet DeepSeek-R1
18.1
8.3
58.6
15.0
22.3
2.6
8.5
3.4
60.4
27.7
10.2
1.4
DeepSeek- V3
7.4
3.2
73.9
15.5
9.0
Aider
Claude-3.5 3oude-3.5-Sonnct -.--onnct
19.9
5.9
42.0
32.1
22.9
1.9
DeepSeck-RI
23.3
8.7
44.5
23.5
27.7
2.7
DeepSeck-Ve
12.5
3.1
54.3
30.1
14.1
1.3
Bolt.dly
22.6
7.6
64.1
5.7
26.4
3.0
DeepSeek-R1
24.7
6.2
64.3
4.8
27.8
2.5
DeepSeek-V3
18.5
4.5
73.9
3.1
20.8
2,0
GPT-4o
10.4
64.5
12.8
03-mini
17.9
3.4
40.0
38.6
19.6
1.6
Owen2,5-Codee-B3.
Coder-32B-Ing
8.2
2.6
81.8
7.4
9.5
1.1
Qwen2.5-72B 1-nrn2.5-72B-Inss Inst.
12.1
3.6
80.7
3.7
13.8
1.4
WebGen-LLM-7B
24.9
7.1
68.0
0.0
28.4
2.5
WebGen-I.M-
25.0
66.3
29.4
WchGen-I.M-32
34.2
8.0
57.8
0.0
38.2
2.8
WebGen-Agent
Proprictary Models
Mlaue-335--Snnee
45.6
12.7
40.6
1.1
51.9
3.9
DeepSeck-R1
40.2
12.4
45.9
1.5
46.4
3.8
DeepSeck-V3
46.1
13.1
40.6
52.6
3.8
03
45.7
11.9
41.6
0.8
51.7
3.5
Gemini-2.5.PP
44.5
12.7
39.4
3,4
50.9
3.8
Claude-4-Sonne
48.8
15.3
33.4
2.5
56.5
4.1
Qwen3-Coder-480B-A
50.5
15.3
34.2
0.0
58.2
4.3
Open-Source Models (10B+)
Qwen2.5-Coder-32BB
26.7
10.5
60.3
2.5
32.0
3.3
Qwens--Coder330B-A3BB-eee
45.7
14.1
40.2
0.0
52.8
4.0
Qwen2.5-72B-Insst.
$7.2
35.9
WebGeen-LM-14B
32,9
10.2
38.9
17.9
38
3.4
WebGen-LM-322
35.4
9.9
54.6
0.2
40.3
3.6
Open-Source Models (10B-)
Owen2.5-CCoder-7BBIB
10.0
60.9
12.4
WebGien-LMM7R
27.8
9.9
51.2
11.1
32.8
3.3
WcbGennAgemt-LM-77e M-7B-SFF
33.8
10.2
56.0
0.0
38.9
3.4
WedGenAAgennLLL--BBSSStppO
40.2
10.5
49.3
0.0
45.4
3.7
Qwen3-8B
29.5
9.1
61.4
0.0
34.1
3.2
WebGenAAgett-M--BB-BB
32.8
11.6
55.6
0.0
38.6
3.4
WebGenAgeei-LM--BB Stepp-GROOO
37.4
12.1
50.5
43.4
filtered out. We observe that this relatively small number of high-quality instructions is sufficient for Step-GRPO training, likely due to the reliable step-level feedback from sereenshots and the GUI agent. Training more samples is costly and does yield noticeable gains.

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Published as a conference paper at ICLR 2026
Table 2: Ablation study on the WebGen-Agent workflow. The configuration starts from exccution- only and incrementally adds capabilities. Accuracy is computed using weighted score, where YES samples are weighted by and PARTIAL samples are weighted by 0.5; the total score is then divided by the number of test cases. The coding LLM used is DeepSeek-V3.
Test Name
Yes
Partial
No
Start Failed
Accuracy
Appearance Score
Execution-only
39.7
12.4
43.3
4.6
45.9
3.0
Screenshot
41.3
10.7
45.9
2.2
46.6
3.6
Screenshot+GUUl-ggeett
43.0
13.9
41.3
1.9
49.9
3,4
Scrennshot-CUUJagguutBBBaaa
45.6
43.1
0.2
3.7
46.1
13.1
40.6
0.2
52.6
3.8
Results. The WebGen-Agent test results are presented in Tab. Based on the results, we make the following observations: (1) WebGen-Agent demonstrates superior performance across various proprietary models compared to other code agent systems. On Claude-3.5 -onnen,e DeepSeek-R1 R1, and DeepSeek- v3, WebGen-Agent significantly outperforms OpenHands, Aider, and Bolt.diy when using the same model. Across all seven proprietary models from five different providers, WebGen- Agent achieves consistently high performance, demonstrating the generalizability of the method. Dwen3-CCoder4800--3335 -0000--35B-Instruu achieves the highest accuracy of 58.2% and an appearance score of 4.3. (2) With 30B-72B sized open-source models, WebGen-Agent also achieves high perfor- mance. On Qwen2.5-Codee 1..55-Coerr32BB --e--32B-Inse Instruct and Qwen2.5-728B 2-77-BBIInttctt WebGen-A Agent outperforms the previous state-of-the art, Bolt.diy, by 22.5% and 22.1% in accuracy, and by 2.2 and 2.0 in appearance scores, respectively. Owen3-Coder-30B. 30B-A3B --Ittrutt achieves the best performance among 30B-72B models, with 52.8% accuracy and an appearance score of 4.0. Also, when using the WebGen-LM models, our WebGen-Agen pipeline consistently outperforms the previous SOTA, Bolt.diy, across all three model sizes. (3) Step-GRPO with Screenshot and GUI-agent Feedback sig- nificantly improves the performance of Qwen2.5-Coder-7BB --r--Coodr----Inttteee Instruct and Qwen3-8B. For Qwen2.5- Coder-7B-Instrune SFT improves accuracy from 12.4% to 38.9% and the appearance score from 1.6 to 3.4; Step-GRPO further improves aceuracy from 38.9% to 45.4% and the appearance score from 3.4 to 3.7. For Qwen3-8B, SFT improves accuracy from 34.1% % to 38.65 38.6% and the appear- ance score from 3.2 to 3.4; Step-GRPO further improves accuracy from 38.6% to 43.4% and the BeboGennAgnnt-M-----Sttteee appearance score from 3.4 to 3.6. Additionally, when we compare WebGenAgenn-778 model GRPO with WebGen-LMM77B which shares the same base model, our outperforms WebGen-LM-7B by 12.6% and 0.4 in accuracy and appearance score. respectively, under the WebGen-Agent pipeline. This further demonstrates the effectiveness our Step-GRPO with Screenshot and GUI-agent Feedback training method. Qualitative analysis of SFT and Step- GRPO's effect in improving the performance is presented Appendix These results demonstrate the effectiveness of our training method in improving both the functionality and appearance of the generated websites. Categorical results are presented Tab. 10 of Appendix
3.2 ABLATION STUDIES
Analysis of the WebGen-Agent Workflow. We analyze various design choices in the WebGen- Agent workflow in Tab. 2 We incrementally add the designs, starting from using only the code execution response messages O ("Execution-onlyy), then gradually adding screenshot feed- back Fshor ("Screenshot"), GUI-agent testing feedback Feu. ("Screenshor+GUUJaggee BawprnshoootGG--gggg----aeeeeeee
the back- and finally the select-best mechanism which makes up the full WebGen- work- flow. As shown Tab. 2 each of these designs yields notable gains in accuracy and appearance. The GUI-agent testing contributes the largest accuracy gain of 3.3%, showing its effectiveness in guiding the functionality the generated websites. The addition screenshot feedback greatly improves the appearance score, raising it from 3.0 to 3.6, demonstrating its effect in enhancing web- site appearance. Adding GUI-agent testing slightly impairs the appearance score, likely because modifying the codebase for functional fulfillment sometimes damages the website appearance or causes errors. This negative effect is mitigated by the addition the backtracking and select-best mechanisms. Qualitative analysis of the effect of screenshot and GUI-agent feedback is provided in
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Table 3: Training strategy ablation on the Owenn.5-Coole-7B-nntttcc model. The configuration starts from the raw model and successively introduces supervised fine-tuning (SFT) and various reinforeement-eermingg variants. Accuracy is computed using weighted score, where YES samples weighted by and PARTIAL samples are weighted by 0.5; the total score is then divided by the number test cases.
Test Name
Yes
Partial
No
Start Failed
Accuracy
Appearance Score
No Additional Training
10.0
4.8
60.9
24.3
12.4
1.6
SET for I Epoch
33.8
10.2
56.0
0.0
38.9
3,4
SFT for 2 Epochs
32.1
14.2
53.5
3.4
SFT with 1.4K Samples Epoch
33.5
11.6
54.9
0.0
39.3
3.4
Naive Outcome GRPO
9.0
53.0
42.5
3.5
Step-GRPO w/ Cumulative Advantage
32.6
12.2
55.2
0.0
38.7
3.5
Step-GRPO Screenshot Reward Only
34.9
10.5
53,9
40.2
3.5
Step-GRPO w/ GUI-agent Reward Only
34.8
11.3
53.6
0.3
40.4
3.4
Step-GRPO Sereenshot+GUJJagge (ours)
10.5
49.3
45.4
3.7
Average File Count
Average Line Count
S-Coder7B.inz.
Qwen3-88
Codir-B-irrt.
Qwen2
Ofigiaal
SFT
Stop GRPO
Figure 3: Comparison of the average file count and average line count among the original, SFT, and Step-GRPO models for Qwen2.5- 2012255-Codrr7/1 - Instruct and Qwen3-8B.
Appendix K Also, as shown in Tab. of Appendix G manual inspection indicates that 98.3% of the sampled instructions achieve high coverage of the requirements.
Analysis of Step-GRPO with Screenshot and GUI-agent Feedback. We analyze the design choices the Step-GRPO with Screenshot and GUI-agent Feedback training process in Tab. 3 The first line shows the result of the 2onem-----Codee----nnntt
model with additional training. The analysis based on Tab. 3 is as follows: (1) The second and third lines present SFT training one epoch and two epochs, showing that training for two epochs does not notably improve perfor- mance compared to training for only one epoch. Therefore, we train for only one epoch in the SFT stage. (2) The fifth and sixth lines show the results of using naive outcome GRPO and Step-GRPO with cumulative advantage. The rewards in these two variants are the same as in our final design (Scoreshot Scoregui); only the advantage computation method differs. Naive outcome GRPO uses the maximum value of the step-level rewards in a trajectory as the outcome reward, setting the ad- vantages the normalized outcome rewards. Step-GRPO with cumulative advantage calculates the advantage of each token as the sum of the normalized rewards from the subsequent steps, as intro- duced Shao al. (024)). Both GRPO advantage computation variants perform notably worse than our final Step-GRPO setting. (3) The seventh and eighth lines present the results of using only the screenshot scores (Scoresho)) or only the GUl-agent testing scores (Scoregui) the rewards. Both are lower than using Score + Score demonstrating the necessity of incorporating both types of feedback. We also gather statistics on the average file count and average line count for the Orig- inal, SFT, and Step-GRPO models, as shown in Fig. 3 For both 5-cen..5CCodr-7B-.
and Owen3-8B, the average file count and average line count consistently increase after SFT and Step- GRPO training. This shows that both the SFT and Step-GRPO stages increase the complexity of the generated websites, which is consistent with their improved performance. (4) We also generate seven hundred new trajectories from instructions in WebGen-Instruc with DeepSeek- V3. Com-
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Table 4: Impact of the feedback VLM on the performance WebGen-Agent on WebGen-Bende
The highest Accuracy and Appearance Score are highlighted in bold.
Coding LLM
Feedback VLM
Yes
Partial No
Start Failed
Accuracy Appearance
Score
Qwen2.5-VL. -inn2.5-VL-32B-Ins. Qwen2.5-VL-32B-BB
4.5
2.2
78.8
14.5
5.6
1.3
DeepSeek-V3
GPT-4o
46.4
11.4
42.0
0.2
52.1
3.6
DeepSeek-V3
Qwen2.5-VVL-22B-nnt.
46.1
13.1
40.6
0.2
52.6
3.8
Table 3: Comparison of using Biven.55VVL-B--nnttree and W Instructt as the feed- back VLM in the Step-GRPO with Screenshot and GUl-agent Feedback training process.
Training Method
Feedback VLM
Yes
Partial
No
Start Failed
Accuracy
Appearance Score
SFT for 1 Epoch
33.8
10.2
56
38.9
3.4
Step-GRPO
Twen2.5-vLL322--nstrun
40.2
10.5
49.3
45.4066)
3.7(-3)
Step-GRPO
Qwen2.5-VIL-7B.
33.7
10.7
54.7
0.9
39.0c...)
3.3con
bining them with the original trajectories, we created scaled training set of approximately 1.4K trajectories. As shown in the fourth line in Tab. B] increasing the SFT data to twice the original size does not significantly improve the accuracy and appearance score of Swen2.5-Coder Coodr--B-Inne IB-Inrruct. This demonstrates that simple distillation has a clear upper bound and cannot achieve the same gain in accuracy and appearance score as our Step-GRPO with Screenshot and GUI-agent Feedback training method.
Analysis of the Coding LLM and Feedback VLM. We analyze the choice of the coding LLM and feedback VLM in Tab. 4 In our experiments, we use relatively small and inexpensive VLM, to provide screenshot and GUI-agent testing feedback, while employing
Qwen2.5-Ve W-V-VVL----nnteeee a strong L.MM capable of generating high-quality code, such as DeepSeek-V. V3, as the coding L.M. As shown in the second row of Tab. replacing Qwen2.5-VL-322 -nssruutttt with a proprietary VLM, GPT-4o, as the feedback VLM does not notably improve the accuracy or the appearance score. This Instruct and GPT-4o in providing
likely because that there is little gap between Qwen2.5-VL-323 ---ttt--nsttuuc appearance and functional judgements, as shown in Tab. 7 and Tab. g of Appendix F This demon- strates that Qwen2.5-VL- L---VVL--BB--trruue is already sufficient for providing accurate screenshot and GUI-agent testing feedback, while being more cost-effective than proprietary VLMs. As shown in the first row of Tab. 4 replacing DeepSeek-V3 V3 with D0we225--VL-32B-Innste icantly worse performance, indicating that the coding LLM cannot be replaced by smaller open- source VLMs. The design choice of decoupling the coding LLM and feedback VLM ensures that code is generated by a strong LLM to maintain quality, while screenshot and GUI-agent testing feedback is handled by a smaller open-source VLM for cost efficiency. As shown Tab. 5 using weaker VLM greatly reduces the effectiveness of the GRPO training, as it introduces noise and inaccuracies the RL supervision signal. Using Owen2.5-VL-77-Immet provides almost no gain in the GRPO training process, demonstrating the major role the VLM plays in our WebGen-Agent pipeline and Step-GRPO with Screenshot and GUI-agent Feedback training approach. Further analysis of the accuracy of the screenshot and GUI-agent scores provided by the feedback VI.M is included in Tab. of Appendix demonstrating the reliability of the scores, and visual judge for images and GUT
results in signif-
as the feedback VLM
quantitative evaluation results of Owwn2.5-VIL-32B-Imme agent trajectories are provided Appendix F
4 RELATED WORK
Visual Code Generation. Code generation that is associated with visual effects exists in wide
range of application scenarios, such as web page development (Lu et al. 2025b; Xu et al.) 2025)
and GitHub issue fixing (Yang et al. 2024d Guo et al. 2025b). Previous work has proposed various
ways to treat visual elements in code generation and other reasoning-intent intensive tasks (Su et al. 2025)
such generating code to represent images in problem statements (Huang al. 2025 Wang al.

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2025b) and using natural language to deseribe images Zhang al. 2024b). We also apply natural language descriptions when providing screenshot feedback. More related to our work, a line of
studies (Guo et al. 2024. Si et al. 2025 Yun et al.] 2024 Beltramelli, 2017 Sun al., 2025 Gui et al., 2025| Laurençon et al., 2024, Wan et al. 2024) explores MILMs ability to reconstruct single-file HTML code from webpage screenshots. Other studies benchmark MILMS' performance in implementing interactive elements in existing web projects (Xiao et al. (025a) or performing web development tasks in pre-defined sequential manner with detailed technical settings (Xiao al.) 20256 Xu et al., 2025 The web development tasks in these works are often solved in a single HTML file (Zhang et al. 2025a) or contain rigid pipelines et al. 2025), which are more suitable for testing MLLMs rather than code agents for end-to-end, repository- website development, as proposed our work. Therefore, we evaluate our agent workflow with WebGen-Bende (Lu et al.) 2025b), which measures a code agent's ability to create multi-file website codebases from scratch and includes diverse website generation instructions.
Code Agents. Equipped with various tools and powered by LLMs (Soni al. 2025 Yao al. 2023 Zhang et al. 2024a), code agents can perform a variety of tasks, such as developing web-
sites (Lu et al.] 2025b) and fixing GitHub issues (Jimenez al. [0244] Yang al. 2024c). Some
code agents specialize in a specific field, such as bug fixing (Zhang et al. 2024c) or machine learn- ing (Jiang et al. 2025). Others, such as OpenHands (Wang al. 2024 and Aider [Aider-Al] 2024).
are general-purpose code agents that are not limited to a single field, though their performance on specific task might not match that of specialist code agents (Lu et al.] 20256). Their relatively low performance in website development is because their tools are not suited to website development, and their general system prompts. Similar to our work, Bolt.diy (stackblitz labs) 2024) specializes in multi-fil file website generation. While it executes the generated codebase, the executed results are, not returned to the model. In contrast, our WebGen-Ar Aegent is a code agent specializing in end-to-end website generation, with screenshot feedback and GUI-agent testing features specifically designed for this task, achieving state-of-the-ane performance.
Fine-tuning and Reinforcement Learning for Agents. Supervised fine-tuning (Pan et al. 2025
Yang et al. 2025b) and reinforcement learning (Dong et al. (025 Qian et al. (2025) are two methods widely used to improve the agentic and tool-calling abilities of LLMs. In the field of code agents,
various works (Pan et al.] 2025 Yang et al. 2025b Zhang et al. 2025b Wang et al. 2025a: Ma
jet al., [024] Xie et al., 2025 Jain et al., 2025 Guo et al. 2025c Ma et al. (025a) leverage su- pervised fine-tuning combined with software engineering data synthesis and rejection sampling to improve the performance of open-source models. Similar to these works, we also use rejection sam- pling and supervised fine-tuning in the warm-up stage before the GRPO training. Other works reinforcement learning with rewards acquired through comparison with the ground truth (Wei et al.p 2025a Ma et al. 2025c. Zhhang al. 2025). determined by the code execution output (Gehring et al.] 2025 Ma et al. |l.] (2255) Golubev et al.) 2025), or dependent on task success (Wei et al. 2025b Lu et al. 2025a Chen et al. 2025). These works either use outcome supervision, which provides sparse training signals, or require detailed ground truth to provide step supervision, which is rigid and difficult to obtain. In contrast these methods, our work leverages screenshot and GUI-agent testing scores at each step- step-which step-level supervision in GRPO training.
which are inherent in the WebGen-Agent pipeline to provide accurate
CONCLUSION
In this paper, we introduce WebGen-Agent a code agent that leverages screenshot and GUI-agent testing feedback, combined with backtracking and select-best mechanisms, to iteratively generate websites with appealing appearance and smooth functionality. We also propose Step-GRPO with Screenshot and GUI-agent Feedback, which leverages inherent screenshot and GUI-agent testing scores to provide step-level supervision in the GRPO training process. Testing WebGen-Agent on WebGen-Bende shows significant improvements across wide range of proprictary and open-source LLMs compared to other code agent systems. WebGen-AAent with Owen3--Coder448B-BB Instruct achieves the best performance, with an accuracy of 58.2% and appearance score of 4.3. Training 2et.5---ooer-BB-nnne
and Qwen3-8B first with supervised fine-tuning and then with Step-GRPO with Screenshot and GUI-agent Feedback notably improves accuracies and appearance scores, demonstrating the effectiveness of our training approach.
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6 REPRODUCIBILITY STATEMENT
To ensure reproducibility, we release the WebGen-Agent workflow code, along with the training code and data for Step-GRPO with Screenshot and GUI-Agent Feedback, as well as the weights of the WebGenAgent-LM models. The complete code base and datasets are provided in the sup- plementary material accompanying this paper. Details of the agent workflow and of all prompts used to deliver multi-level feedback are presented in Section 2. Appendix B and Appendix [ The training procedure for Step-GRPO with Screenshot and GUI-Agent Feedback is described in Section 2.2 and Section 3.1 We also report manual inspection of the screenshot and GUI-agent scores in Appendix E and we assess the comprehensiveness of the GUI-Agent testing instructions in Appendix G Collectively, these resources ensure that our findings are transparent, robust, and independently verifiable.
ACKNOWLEDGEMENTS
This study was supported in part by National Key R&D Program of China Project 20227D0166 100, in part by the Centre for Perceptual and Interactive Intelligence, CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Govermment in part by NSFC-RGC Project N CUHK498/24, and in part by Guangdong Basic and Applied Basic Research Foundation (No. 202381515130008, XW).
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John Yang. Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. Swe-agent: Agent-computer interfaces enable automated software engineering. quet, J. Tomezak, and C. Zhang (eds.), Advances in Neural Information Process-
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A LIMITATIONS AND FUTURE WORK
WebGen- Aegntt specifically designed to generate websites based on natural language instructions from non-expert users. We do not consider website response speed or complex network conditions when generating and evaluating the websites; these are interesting questions for future work. In the supervised fine-tuning and Step-GRPO experiments, we train only 7B- and SB-parameter models due to limited computing power and GPU memory, as Step-GRPP training would take more than 24 hours 16 NVIDIA A800 GPUs, and we currently do not have enough GPUs to train larger models. The results on the 7B and 8B models show great potential for our method, and we plan to apply our training approach to 30B-72B models in the future. Moreover, the functionality reward depends the capability of the GUI agent. While our experiments show that even with the current Web Voyager agent, the functionality reward provides meaningful and consistent feedback and learning signals, we also note that our design is agent-agnostic: as GUI agents improve, the functionality reward naturally becomes more accurate and more informative. This means that the performance of our method can continue to improve with the advancement of GUI agents, which can be explored in future work.
B WEBGEN-AGENT ALGORITHM
Algorithm demonstrates the WebGen-Agent inference workflow in detail. Algorithms 2 and two helper functions for Algorithm presented separately for clarity.
are
C WEBGEN-AGENT PROMPTS
The prompts for acquiring screenshot and GUI-agent testing feedback are presented in Fig. Fig. and Fig.
Fig.
D EXAMPLES OF WEBGEN-AGENT TRAJECTORIES
To demonstrate the WebGen-Agent workflow in straightforward way. we present three example trajectories in Fig. 8 Fig. D and Fig. I0 As shown these examples, WebGen-Agent iteratively
improves the appearance and functionality of the generated website based on screenshot and GUI- agent testing feedback.
E ACCURACY OF SCREENSHOT AND GUI-AGENT TESTING SCORES
To analyze the accuracy of the screenshot and GUI-agent testing scores given by the feedback VLM the WebGen-Agent workflow, we evaluated the results of Claude-4-Sonnet, Qwen3-Coder 30B-A3B-Instruct, Qwen3 Wwee3-Coodr-480BBB A35BB Insttuutt and DeepSeek- V3 as coding LLMs, with Dwwn2.5--LL322B- Instruutttt as the feedback VLM, as well as DeepSeck- V3 as the coding LLM and GPT-4o the feedback VI.M. We manually verified the accuracy of the screenshot and GUI-agent testing scores. Human annotators were provided with the score and the screenshot or GUI-agent trajectory at each step and asked judge whether the score was accurate. If the score was inac- curate, they provided the correct score. The results are presented in Table 6 The manual testing results serve the ground truth and require precision; therefore, three human testers independently annotated the results, and we assessed the consistency of their annotations. If the annotations of test case are inconsistent, fourth human tester is tasked with re-examining the test case and the inconsistent annotations to decide a final annotation. The annotation guidelines are presented in Fig. 12 and Fig. [4] while the annotation interface screenshots are presented in Fig. [T and Fig. [3] The testers student volunteers with bachelor's 's degrees in computer science-related majors who are studying for a master's degree. We also compute Cohen's 's x between the three human annota- tors evaluate inter-annotator agreement, shown in Table A and Table B below (Cohen's 's 1-2 denotes Cohen's 's R between annotator and annotator 2; the others are similar). All of Cohen's 's k values above 0.8, with most above 0.9, demonstrating strong inter-annotator agreement.
The accuracies of the screenshot scores across all experiments ranged from 93% to 96%, while the accuracies of the GUl-agent scores ranged from 89% to 93%. The standard errors of the screenshot
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Prompt:
You given single website screenshot input.
Task
1.
Examine the sereenshot closely for any rendering or runtime errors (c.g., "404 Not Found", stack traces, missing styles, blank areas).
2. Decide whether screenshot shows rendering or runtime error.
If yes, "is errer": true,
paraphrase the visible message(s) into "error www.amesssaee... and leave "screenshot www.:eemsssot.dddeerii...... empty.
If no, set "is.error"; false, leave "errerrmeessaseee as an empty string (""), and write concise but thorough "screenshhot.descriiiiiii that covers:
Overall layout (e.g., header/sidebarrfooteer,, grid, flex, single-column).
Key UI components (navigation bar, buttons, forms, images, cards, tables, modals, etc.).
Color scheme and visual style (dominant colors, light/dark theme, gradients, shadows).
Visible content and text. (headings, labels, sample data).
Notable design details (icons, spacing, font style) that help someone understand what page looks like).
3. Suggest ways improve the appearance of the website, for example:
Separate incorrectly overlapping components.
Adjust layout to avoid large blank areas.
Adjust text or background color to avoid text color being too similar to the background color.
If improvement is necessary, leave "suggestion an empty string (""";; otherwise, briefly list the suggestion(s) "suggestions".
4. Grade the response.
Output format (valid ISON)
'json
" "is_error"; <boolean>,
"error_mess serror_meesage: "<string>",
"screenshot "scrennhht__escrriiion description": "<string>", " "suggestions": "<string>"
Return only this JSON object- no additional commentary, markdown, or code fences.
Figure 4: The prompt for generating the description and suggestions based on the website screen- shot.
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Prompt:
You tasked with evaluating the design of webpage. Grade the webpage's appearance scale of to (5 being highest), considering the following criteria:
Successful Rendering: Are there any components in the page or is completely blank? Does the webpage render correctly without visual errors? Are colors, fonts, and components displayed as specified?
Does the design align with the website's purpose and user requirements? Are elements (e.g., search bars, report formats) logically placed and functional?
Content Relevance:
Layout Harmony: Is the arrangement of components (text, images, buttons) balanced, intuitive, and clutter-free?
Modernness & Beauty: Does the design follow contemporary trends (c.g., minimalism, respon- sive layouts)? Are colors, typography, and visual hicrarchy aesthetically pleasing?
Grading Scale:
(Blank Page): The screenshot is completely blank or does not contain any visible content.
may only have a background color or display an error message.
(Poor): Major rendering issues (e-g.. broken layouts, incorrect colors). Content irrelevant or missing. Layout is chaotic. Design is outdated or visually unappealing.
(Below Average): Partial rendering with noticeable errors. Content partially relevant poorly organized. Layout lacks consistency. Design is basic or uninspired.
(Average): Mostly rendered correctly with minor flaws. Content relevant but lacks polish. Layout functional but unremarkable. Design clean but lacks modern flair.
(Good): Rendered well with no major errors. Content relevant and logically organized. Layout is harmonious and user-frientt seienfriendly. Design is modern and visually appealing.
needs. Layout polished, responsive, innovative. Design is cutting-edge, beautiful, and memorable.
(Excellent): Flawless rendering. Content highly relevant, intuitive, and tailored
Task:
Review the provided scrcenshot(s) of the webpage. Provide a concise analysis of a few sentences and then assign grade (0-5) based your analysis. Highlight strengths, weaknesses, and how well the design adheres the specifications.
Your Response Format 'jjoon
"analysis"; "<string>" "grade": <int>
Your Response:
Figure 5; Prompt for evaluating the visual quality of webpage and generating an appearance score.
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Algorithm WebGen-AAeent
Require: Initial instruction I, maximum steps T
Ensure: Final codebase C"
1:
[Z]
trajectory: instruction, edit, feedback, P archive of step snapshots P current codebase
Steps +
3: C -
4: t +-1 1, consecErr +- O
5: while & < T do
6:
AC GENERATREDITO! T += AC,
8:
<- APPLYEDIT(C, ACI) O EXECUTE(C)
9:
10:
if error then
T+
12:
consecErr + consecErr +
13:
if consecEsr = then
14:
( + SELECTBESSTSTER(SSeS C C* TRUNCATE(TT,")
15:
restore codebase
17:
+ 1' +1, consecErr +- 0
18:
else
19:
++++1
20:
end
21:
continue
22:
else
23:
consecErr
24:
end if
25:
ing +- SCREENSHOT(C)
26:
(desc, sugg sugs sar shore + VLM.Ju)
27:
T +- (dese, sugg
28:
goNext AGENTDECISION(T)
29:
if not goNext then
30:
f. 6-6- + 1; continue
31:
end if
32:
(pass, sugg gui sc)
GUI AGENT(C)
33:
T+- (pass, sugg
34:
Steps += (t, Lt,C, scorega)
35:
if pass then
36:
break
37: else
38:
f 1-1+1
39:
end if
40: end while
41: (=.C*.
..*)
SELECTTESSTSTE<(See)
42: return
Algorithm SELECTBESTSTEP
Require: Steps (,t,C,scoren, scoregui))
I: Imax f- maX scoregu
2: Sg + {8 score = Imax
3: return arg max scoresat <ES,
scores range from 0.20 to 0.26, while the standard errors of the GUI-agent scores range from 0.31 to 0.44. This demonstrates that the scores are highly accurate, supporting the effectiveness of the WebGen-Agent workflow and the Step-GRPO training process. Compared with using Qwen2.5-VLL Instructt using GPT-4o as the feedback VLM only marginally improved the screenshot score accuracy from 94.8% to 95.5% and the GUI-agent score accuracy from 91.2% to 92.2%. This shows that Qwen2.5-VL--22B Instturt is sufficient for the task while being significantly more cost-effective.
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Published as a conference paper at ICLR 2026
Algorithm 3 TRUNCATE
Require: Trajectory T. step id t*
I: return prefix of T ending just after the edit and feedback of step 8*
Based on the original website development instruction, you should identify all the requirements of the website generation and create comprehensive instruction for weh-navigation GUI agent the generated wehsite. The following is an example triggering the GUT agent testing based on original instruction:
Example
Original instruction:
Please implement a self-driving tour website that provides self-driving tour products and services. The website should have functionalities for browsing self-driving tour routes, booking self-driving tour hotels, and self-help self-driving packages. Users should be able browse different types of self-driving tour routes, book hotels packages, and query self-driving club information. The website should also provide search and filtering functions to help users quickly find the self-driving tour products they need. Define background as cream, define components with dark teal.
<boitAct ion "ype="gui.agent.e
Verify cream background and dark-teal buttons. Browse different types of self-driving tour routes, hook hotels and packages, and query self-driving club information. Search and filter for self-driving tour products. </boltActicn>
The following is the original website development instruction:
cinstruction ion> inntruuction </ inst rucc ion>
Trigger the GUI agent testing based on the original instruction in a way similar to the example. Do not generate additional comments.
Figure 6: Prompt for generating a GUl-agent testing instruction from the original website specifica- tion.
F RELIABILITY OF THE CHOSEN VLM
To evaluate the reliability of the screenshot and GUI agent feedback, we test the performance of chosen VLM, Sw-n2.5-VL-32B-B15
on PairBench (Feizi al. 2025) and AgentReward- Bench (Lù et al. 2025), and present the results in Tab. 7 and Tab. g below. Results of the closed- source models are taken from Feizi al. (2025) and [å et al. (2025). Tab. shows the aggregated MMScore, 1-RelaxSym, Smoothness, and Controllability over all four data splits in PairBench. Tab. 8 shows the VLM judge performance for predicting success, mainly measured with preci- sion Là et al. 2025).
As shown in Tab. and Gemini- 1.5- on three out of four metrics. Notably, on MMScore, which is the main metric in the PairBench paper (Feizi et 2025), Dwen2.5-VL-325 WVL33B-Innstruc outperforms GPT-4o by 20.03% This shows that Qwen2.5-Vi VL- B- is good at comparing similar images, making it a suitable choice for providing screenshot feedback and appearance scores in the WebCien-Agent pipeline.
outperforms closed-source models such as GPT-4o
Additionally, demonstrated in Tab. g 2ree22.5-VL-3221 Instruct outperforms GPT-4o, GP mini, and --ntb--tu in Overall Precision, which is the main metric used in Lu et al. 2025) to judge model's ability to predict the success of web agent trajectories. Its performance is only 1.1% lower than Claude-3.7-Somme source models. On the five data splits, Owen2.5-VL-32B-In Instrrcct achieves the highest performance on VWA (VisualWebAremm, and Wk++ (WorkArema++)) and is only lower than the highest precision on Work Arenk) by 0.5%. This demonstrates that 2--..55-VV--3--nnsttmae is reliable
while being much more cost-effective than the closed-
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Published as a conference paper at ICLR 2026
Prompt: You given GUI-agent testing trajectory.
The GUI agent testing trajectory:
GUL-agent Testing Instruction:
(gui_instruction]
Trajectory: (resuit}
Task
Examine trajectory for any failed actions that indicate problem website design.
2. Decide whether the GUI-agent testing trajectory reveals any flaw in the website implementation.
If yes, set "test.passede
true, and "improvement.sugger innessst empty. If no, set "test :aased"" false. and write concise but thorough "improvement.o www.oo/mmmen.uuggstiiiooo..o suggestionn"" that covers the suggested improvements targeting the problems revealed by the testing result.
3. Evaluate the results of the GUl-agent test run and assign one integer grade from I to 5:
vast majority tested functions
behave incorrectly.
Many functions fail; only a hehave as expected.
3: About half of the functions work as expected; success mixed.
4: Most functions work as expected; only minor issues remain.
5: All tested functions work exactly expected; no issues observed.
Assign the grade " "grade".
Output format (valid JSON)
'jsonn
'test_passed" <boolean>, tmmprovment__argestiion "<string>", "grade": <int>
You can first make short analysis of three sentences, then output this JSON object.
Figure 7: Prompt for evaluating GUI-agent testing trajectories and providing improvement sugges- tions.
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Table Accuracy of the screenshot and GUl-agent scores using human annotation as ground truth. For every experiment we report the accuracy together with the standard error and inter-rater agree- ment (Cohen's K). K1-2 denotes Cohen's between annotator and annotator 2; the others are similar.
Score
Coding L.L.M
Feedback VLM
Acc. (%)
Std.
K1-2
61-3
K2
Avg.
Type
Err.
P
Screenshot
Claude-4-Sonee
Dwen2,5-VL-322B-2
93.6
0.25
0.959
0.964
0.933
0.952
Screenshot
10a3--Coee-330--AA
Qwven2.5-VL-32B--m Ins.
93.9
0.26
0.964
0,984
0.978
0.975
Screenshot
Qwen3-Codee Bwen--Codrr-480-A35BB
Dwen2.5-VLL332B-mm
95.6
0.20
0.948
0,953
0.912
0.938
Screenshot
DeepSeek-V3
Qwven255-VL-32B-Inst
94.8
0.22
0.968
0,982
0.985
0.979
Screenshot
DeepSeek-V3
GPT-4o
95.5
0.20
0.980
0.986
0.970
0.979
GUT agent
Claule-4-Sonee
Qwen2,5-
90.1
0.31
0.973
0.946
0.932
0.950
GUT agent
Dwwo2.5-VV-332B-Im
91.4
0.44
0.933
0.885
0.941
0.920
GUT agent
89.6
0.41
0.967
0.971
0.943
0.960
GCT agent
DeepSeek-V.
Owen2.5-VL-332B.
91.2
0.36
0.947
0,960
0.911 0.939
GUT agent
DeepSeek-V
GPT-4o
92.2
0.33
0.948
0.951
judge of the GUI agent trajectories and can provide accurate functional feedback for our WebGen- Agent pipeline.
Table 7: Comparison of the aggregated MMScore, I-RelaxSym, Smoothness, and Controllability over all four data splits in PairBench (Feizi et al. 2025). Results of the closed-source models are taken from Feizi al. (2025).
Model
MMScore (%)
1-RelaxSym (%)
Smoothness
Controllability (%
GPT-44-mini
48.28
89.07
1.59
85.48
GPT-4o
53.95
91.53
1.54
72.77
Ge5ini-l,5-Fllsh
56.55
93.19
1.34
74.54
Gemini-1.5-Pro
52.60
88.72
1.17
88.09
Qwen2.5-VL-32B- Instrcctt
73.98
91.30
2.86
93.37
G ANALYSIS OF THE COMPREHENSIVENESS AND ACCURACY OF GUI-AGENT TESTING INSTRUCTIONS
To analyze the comprehensiveness. of the GUT-agent testing instructions generated by the agent, manually evaluated the instructions from the experiment using Claude-4-Sonnet, Qwen3- A---800B--3B---tntnccc Instuunst and DeepSeck-V3. V3. We graded each GUI-agent instruction on a 1-5 scale, determined by how completely the instruction translates each website requirement into concrete GUI-agent checks. The grading guidelines are presented Fig. 16 and the annotator interface screenshot is presented in Fig. 15
Swem3-Coder-480B-AN3
As shown in Tab. 9 77.2% of the GUI-agent testing instructions across the four models receive score of 5 (Complete, 100% of requirements). Instructions with score of 4 or higher (High, 75-90%) account for 98.3% of the total, while only 1.7% receive score of 3 (Moderate, 50-75%); none score below These results indicate that the GUI-agent instructions comprehensively cover most of the website requirements.
To analyze the accuracy of the generated test cases, we also randomly sampled 100 test cases, and inspected them manually. We judge the test case of website development instruction accuracy if fulfills the following two conditions: (1) The test case covers all the requirements the instruc- t2on.(2) Each part of the test case corresponds to requirement in the instruction. The accuracy of the 100 test cases is 87%. Among the 100 test cases, only 13 show slight mismatches with the website development instructions, further demonstrating the effectiveness of the generated test cases.
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Table 8: Detailed precision, recall and Fl comparison across models.
Model
Overall
Overall
Overall F1
AB
VWA Preci- sion
WA Preci-
Work Preci-
Wk++
Preci- sion
Re- call
Preci- sion
Preci-
sion
sion
sion
Claude-3.7-Sonect
69.4
76.3
72.7
71.4
64.8
69.3
85.3
66.7
GPT-4o
68.1
80.3
73.7
77.8
60.7
69.9
93.8
59.6
GPT-4o-mini
64.5
78.3
70.8
80.0
57.4
66.9
90.3
54,8
Swen2.55VL-722B-nsiile
64.5
86.1
73.7
70.0
58.5
62.9
93.8
64.4
Qwen2.5-v1-322B-mmmrr
68.3
79.7
73.5
70.0
68.2
61.8
93.3
66.7
Table 9: Distribution (%) of human scores regarding the comprehensiveness of the GUI-agent testing instructions and the resulting average score, and inter-rater agreement (Cohen's k). The definition of the scores is presented in Fig. [6] The scores range from 1 to 5, K1-2 denotes Cohen's K between annotator and annotator 2, the others are similar.
Model
3
Avg. Score
R1-2
K1-8
R2-3
Avg.
KC
Claude-4-Sonnt
84.2
13.9
2.0
0.0
0.0
4.82
0.873
0.807
0.930
0.870
DeepSeek-V2
73.3
24.8
2.0
4.71
0.940
0.922
0.864
0.908
75.2
23.8
1.0
0.0
0.0
4.74
0.914
0.914
0.880
0,903
76.2
21.8
2.0
0.0
0.0
4.74
0.868
0.822
0.860
0,850
Total
77.2
21.0
1.7
0.0
0.0
4.75
0.898
0.881
0.871
0.883
H CATEGORICAL RESULTS
Tab. 10 shows the categorical results of WebGen-Agent with various proprietary and open-source models WebGen-Bende As shown in the table, WebGen-Agent consistently achieves superior performance across all instruction and test-case categories compared to other code agent systems. For both the 7B and 8B models, Step-GRPO improves performance most categories compared to the original instruct model and the SFT model. This demonstrates the effectiveness of the WebGen- Agent workflow and the Step-GRPO training process, which incorporates screenshots and GUI- agent feedback.
ANALYSIS OF MAXIMUM ITERATION NUMBERS
To analyze the effect of the maximum iteration number parameter on the performance of WebGen- Agent, test the accuracy, appearance score, and the percentage of samples that exceed the maxi- mum iteration limit (exceed rate) at different maximum iteration numbers. The coding LLM used is D3e
As shown in Fig. 17 and Tab. 11 the accuracy and appearance score show a rising trend as the maxi- mum iteration number increases, while the exceed continuously decreases. When the maximum iteration number is between 14 and 20, the accuracy, appearance score, and exceed rate all begin to converge. This is because exceed rate, and the impact of the maximum iteration number on performance diminishes.
samples finish before reaching the iteration limit, reflected by the
QUALITATIVE ANALYSIS OF SUPERVISED FINETUNING AND STEP-GRPO
To provide qualitative analysis the effects of supervised fine-tuning and shot and GUI-agent feedback, present examples of websites generated by Owen2.5-Coder-2 Instruct, WebGenAggen-LMM-7B.B SFT, and WebGenAAgentLM-77B -B--T--tepp------- Step----RRPOO Figs. 18 and 19 LLMGGenAggnt-MM-B--SS-S
with screen-
We also include examples of websites generated by Qwen3-8B, B-M----tte--GPOO in Figs. 20 22 As demonstrated in the examples, super- vised fine-tuning greatly reduces the models' tendency generate erroneous malformed web-
and
23

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Published as a conference paper at ICLR 2026
Table 10: Categorical results of WebGen-Agent with various proprictary and open-source models on WebGen-Benel (Lu et al. (2025b) compared with other code agent systems. The highest score of each column is marked bold.
Instruction Categories
Test-case Categories
Test Name
Content
User Inter- action
Data
Functional
Data- Display Testing
Design-
Presen- tation
Manage- ment
Testing
Validation
OpenHands
Blaude335-Soonee
32.8
18.4
18.4
12.4
33.9
32.0
DeepSeek-RI
16.4
8.9
5.9
5.0
9.9
25.0
DeepSeek-V?
12.6
7.3
3.8
8.1
25.0
Aider
Claude-355-Sommt
31.9
21.1
16.6
14.9
30.1
34.0
DeepScek-R1
39.1
28.6
13.4
17.6
35.2
44.3
DeepSeek-V V3
12.8
12.5
9.7
18.4
Bolt.diy
Hlude-3.5-Somnee
35.6
21.2
26.2
17.1
26.3
52.0
DeepSeek-RI RI
43.7
20.6
24.7
21.1
29.3
44.3
DeepSeek-V.
37.1
16.6
10.5
28.2
GPT-4o
26.4
5.9
11.2
4.7
19.6
24.6
03-mini
28.7
17.7
13.4
11.4
25.5
33.6
Owwn2.5-Coder-322
17.5
6.9
5.9
1.9
14.5
23.0
Owen2.5-72B-Inst.
28.2
10.1
5.6
5.8
21.0
25.4
WebGen-LM-7B
27.9
23.8
22.0
27.7
WebGen-LM-14P
30.2
27.8
31.6
23.6
26,9
49.2
WebGen-LMM-3
46.6
33.2
38.8
29.1
43.0
56.1
WebGen-Agent
Proprietary Models
Claude-3.5-Sonnee
57.8
48.7
51.9
38.5
60.5
76.2
DeepSeek-RI
57.8
44.2
35.0
53.8
DeepSeek-V V3
58.0
53.2
45.6
40.9
61.0
72.5
o3 Claude-4-Sonner
59.2
46.6
53.4
43.7
55.1
68.9
68.7
51.8
52.5
44.0
69.4
71.7
Gemini-2.5-Pro
60.3
48.2
37.9
60.2
Qwen2-CCuer-400---35--
64.7
55.8
43.2
71.2
Open-Source Models (10B+)
Qwen2.5 Swen2.5-Coder-3221 -odeer-32B-Inst
35.6
28.8
34.4
20.9
32.3
62.3
Owen3-Coder
Coder-30B-A3B-Imm
55.2
54.3
39.1
62.1
Swen2.5-72B-IInttee
43.4
30.4
38.8
23.0
39.8
66.0
WehGen-LMM-14B
44.3
31.3
44.4
28.6
40.1
61.1
WebGennLLM-32B
47.1
37.2
39.1
24.6
45.7
75.8
Open-Source Models (10B-)
20.7
8.6
10.9
7.4
15.9
21.3
WebGen-LM-7B
33.9
27.5
41.9
22.0
34.7
59.8
WebGennggem-LM47BBSSIT
53.4
33.5
33.8
23.5
48.4
67.6
51.1
41.1
47.8
30.7
56.7
69.3
Qwen3-8B
37.4
34.3
30.0
26.8
34.1
54.1
WebGenAgent-LMM L----A---e-------FFF--
41.7
34.2
43.8
26.8
43.8
63.1
WebGenAgeett-MM LM----g-ett------- -M---tt-LLL-----------R--- SB-Step-GRPO
52.0
38.8
43.1
30.2
51.1
68.4
sites and improves their ability to follow the appearance requirements specified the instructions. Step-GRPO further refines the aesthetics and harmony of the generated websites.
24

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Published as a conference paper at ICLR 2026
Table 11: Influence of the maximum number of iterations on WebGen-Agent performance. The coding LLM used is DeepSeek-V3. V3.
Metric
10
12
14
20
Accuracy (%)
42.4
47.9
50.2
51.9
51.2
53.3
52.6
52.6
Appearance Score
3.2
3.6
3.7
3.6
3.7
3.8
3.8
3.7
3.8
3.8
Exceed Rate (%)
100.0
57.4
34.7
22.8
15.8
13.9
10.9
8.9
8.9
7.9
K QUALITATIVE ANALYSIS OF THE WERGEN-AGENT WORKFLOW
To demonstrate how the WebGen-Agent workflow functions, provide examples of steps in WebGen- trajectories where the agent improves the website's appearance based on screen- shot GUl-agent feedback. As shown Fig. 22 Fig. 23
Fig. 25 and Fig. 26 the agent enhances the website's s visual appeal by incorporating suggested improvements. Similarly, Fig. 27 Fig. 28 Fig. 29 Fig. 30 and Fig. 31 illustrate how the agent refines the website's functionality based on feedback from the GUI-agent lesting process. The steps are simplified due to space constraints.
Fig. 24
L DATA LEAKAGE EVALUATION
The 700 trajectories used in SFT training generated from website generation instructions taken from SubGen-Instrue which is introduced in the WebGen-Bench (Lu et al. (025b) paper. As explained in Lu et al. (0255)), meticulous decontamination steps have been taken the construc- tion of WebGien-Istruut.. They first employ 5-gram Jaccard similarity, removing the instructions in the training set with similarity score higher than 0.6 with of the instructions in the test- ing set. Then, to remove the instructions that are semantically similar to those in the testing set, they compute the sentence embeddings of the instructions using the all-MiniLM--6-v2 model of Sentence- Transformer (Reimers & Gurevych 2019), removing the training instructions with co- sine similarity of larger than 0.55.
To further ensure there is no data leakage, we also apply the n-gram testing, as demonstrated in the table below. The overlap percentages various n-grams are quite low, and the overlap becomes 0.00% when n-grams are 13.
Table 12:
Overlap ratios between the instructions in the training and the WebGen-Benn test set for various n n--rrmm sizes.
n-grams
7
11
13
Overlap Ratio (%)
5.59
1.47
0.22
0.00
M GENERALIZABILITY OF WEBGEN-AGENT
To demonstrate the generalizability of our method, we test WebGen-Agent on the Web Application subset of the ArtifactsBench (Zhang et al., 2025a) dataset, which also requires generating a web- site based on user instructions, though their requirements are less complicated than the instructions in BebGeen-Bench. The baseline results of the closed-source models and DeepSeek-V V3 are taken from Zhang et al. (2025a), while the others we tested using their open-sourced codebase. As shown in the Tab. 13 WebGen-Agent with Dwen3-Coder-4880F A35-00-----B-tttttete the best performance of 63.86, outperforming the baseline results of various closed-soune source models and the baseline result of Qwen3- Conn--Coder-888- 480d-e-4800-A355 A-5B----Btn---- WebGen-Agen with DeepSeek V3 also outperforms the baseline result DeepSeek- V3. This demonstrates the effectiveness and gen- eralizability of WebGen-Agent. We have added this to the revised paper.
as the coding LLM achieves
25

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Published as a conference paper at ICLR 2026
Table 13: Performance of WebGen-Agent on the Web Application subset of the Artifacts- Bench (Zhang et 2002a) dataset. The baseline results of the closed-source source models and DeepSeek- V3 are taken from Zhang al. (2025a), while the others are tested using their open open-sourced code- base.
Test Name
Average MLLM score
GPT-4o ol
39.27
39,01
Claude 3.7-Sonnet
53.64
Claude 4.0-Sonnet
55.79
Gemini-2.5-Pro DeepSeek-V. V3
58.30
47.47
DeepSeek-V3+ WebGen-Agent
55.74
(wen3--oder--80BA33B-----
55,42
WebGen-Agent
63.86
Table 14: Runtime, cost, compute, and qualitative scores for each model/agent configuration. Num- bers in parentheses denote the gain WebGen-AAent over the corresponding Bolt.diy baseline.
Test Name
Avg. Run- time (min)
Avg.
Avg.
Accuracy Appearance Score
Cost ($)
FLOPs (TFL.Ps)
Mlude-3.5-Someee Bolt.diy
2.1
0.0677
N/A
26.4
3.0
Claude-3.5-Sonnct WebGen-Agent
13.1
0.2009
N/A
51.9(42555
3.9(409)
DeepSeek-R1 + Bolt.diy
6.3
0.0025
1.55 x 10
27.8
2.5
DeepSeek-R1 + WebGen-Agent
17.5
0.0057
3.54 x 10
46.4c.18.6)
3.8(-1.3)
DeepSeek-Ve V3 + Bolt.diy
3.2
0.0088
3.18 x 10
20.8
2.0
DeepSeek-V3 WebGen-Agent
13.3
0,0131
2.33 x 10
52.6++318)
3.8(+18)
Oween2.5-Cooder-32B--nn... Bolt.diy
1.4
N/A
8.18 1014
9.5
1.1
Oween2.5-CCxdee332BB-nsss WebGen-Agent
16.7
N/A
2.47 x 10
32.0G22.5)
3.3(422)
Qwen2.5-72B Inst. Bolt.diy
2.9
N/A
1.90 10
13.8
1.4
Qwen2.5-72B + WebGen-Agent
19.0
N/A
6.54 10
35.9(42.1)
3.4++2.
N EFFICIENCY ANALYSIS
We present the average runtime, average cost, and average FLOPs in the table below, as well as the accuracy and appearance scores. As demonstrated in Tab. 14 the average runtimes of the WebGen- Agent pipeline are all between 10 to 20 minutes, and the average costs are all between S0.01 and SO.3, which within the acceptable range for developing a multi-file codebase of an interactive website from scratch. The time used for Claude-3.5-Sonnet and DeepSeek-Vi V3 is around 13 min- utes, as they have strong coding abilities and make fewer mistakes, thus requiring fewer iterations. DeepSeck-RI's average runtime is longer, it is reasoning model and needs to generate longer thoughts. 5-
and Qwen2.5-72B Instrucct are weaker models and need more iterations to correct errors and improve the websites.
The average runtimes and average costs of WebGen-Agent are notably higher than those of Bolt.diy, but WebGen-Agee Aggentt--gettt significantly improves the accuracy and appearance scores compared to Bolt.diy, bringing around 20% to 30% rise in accuracy and around a 1.0 to 2.0 rise appearance scores. Therefore, believe that this significant increase in performance justifies the increased cost and runtime. We have added the results to Appendix N in the revised paper.
RESULTS OF DIRECT RL TRAINING WITHOUT SFT
We have finished running the experiment directly training the original model (without special SFT) using Step-GRPO with Screenshot and GUI-agent Feedback training approach. We per-
26

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Published as a conference paper at ICLR 2026
Table 15: Result of training directly with RL, without SFT warm-up, on Qwen3-8B. "Accuracy" is weighted metric where Yes answers count 1.0 and Partial answers count 0.5, divided by the number of test cases.
Test Name
Yes
Partial
No
Start Failed
Accuracy
Appearance Score
Qwen3-8B Qwen3-8B Step-GRPO
29.5
9.1
61.4
0.0
34.1
3.2
34.2
12.7
52.4
0.8
40.5 (+6.4)
3.5 (10.3)
Qwen3-8B SFT
32.8
11.6
55.6
0.0
38.6
3.4 3.6 (40.2)
Qwen3-8B SFT Step-GRPO
37.4
12.1
50.5
0.0
43.4 (14.8)
formed RL training directly Qwen3-8B, and the results presented below. As shown in the table below, applying Step-GRPO with Screenshot and GUl-agent Feedback directly on Qwen3-8B achieves rise in accuracy of 6.5% and rise in appearance performance is even more pronounced than training on the SFT model. This demonstrates that the distillation DeepSeek-Vi V3 is not absolutely necessary, and that the same gains could likely be obtained when applied to larger model.
of 0.3. This improvement in
P THE USE OF LARGE LANGUAGE MODELS IN PAPER WRITING
The paper is primarily human-written However, large language models such of A 2025a) and DeepSeek-Ve V3 (Liu et al.22444) are used to check for grammar and spelling mistakes. The words and phrases are occasionally polished by LLMs to make the wording more fluent.
27

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Published as a conference paper at ICLR 2026
System Prompt: You Bolt, expert assistant and exceptional senior software developer with knowledge across multiple programming languages, frameworks, and best practices. <system_conseee constralnts> You are operating In environment called WebContainer...
User: Please implement a food distribution website for managing and distributing food. The website should have functionalities for food donation, food distribution, and volunteer management. Users should be able to browse food donation Information, apply for food distribution, view volunteer information, and manage their own accounts. Use azure for the background and midnight blue for components.
Assistant: I'll create food distribution website with the requested functionalities and color scheme. Here's the implementation plan: Set up Vite React with required dependencies 2. Create components for donation, distribution, and volunteer management
Installation of dependencies was successful. Starting the service was successful. The screenshot description: The webpage has a clean and simple layout with a dark blue header containing navigation links: 'Home', 'Donations', 'Distribution', 'Volunteers', and 'Login' -.... Suggestions for Improvement: Consider adding more visual elements such as icons ..... no further modification necessary, output <boltAction type= "screenshot, prreenshot_valldated validatedd that the screenshot satisfactory.
signal
Score shot 3, S.ore gut
Assistant: I'll enhance the design with more visual elements while maintaining the color scheme. Here the key improvements: 1. Add food-related icons to 2. Add cards for featured
Installation of dependencies was successful. Starting the service was successful. The screenshot description: The screenshot shows clean and organized homepage for website called 'Food Distribution Network." The overall layout includes a top navigation bar with icons and links labeled 'Home,' "Donations," "Distribution," 'Volunteers, navigation bar, there -... no further modification necessary, output <boltAction type= screenshot_ caaiiitt_lviiiddd/// /> to signal that the screenshot satisfactory.
'Login.' Below
<boltAction type= "screenshott validated"/>
The suggestions based on the GUl-agent testing result: Address the root cause of the blank page issue, which could involve server-side errors, frontend rendering problems, or resource loading failures. Implement robust error handling and loading indicators provide feedback users. Ensure the website thoroughly tested staging environments catch such critical issues before deployment...
Dturibddon
CliCK
shot 5, Score
Assistant: I'll error handling and loading states to prevent blank page issues. Here are the key improvements: 1. Add loading all pages ......
Installation of dependencies successful. Starting the service was successful. The screenshot description: The screenshot shows clean and organized homepage for food distribution network. The overall layout includes top navigation bar with icons and links labeled 'Home', 'Donations', 'Distribution', 'Volunteers', and 'Login'. Below the navigation bar, there is prominent heading 'Welcome to...... If no further modification is necessary, output <boltAction type= ape==scrrenshht. validated" //> to signal that the screenshot is satisfactory.
<boltAction type= sscrensshte /
Wel coras Dirmbariee Network
Click [1|
Click [2]
Type (3) admin
The suggestions based on the GUl-agent testing result: Improve the Food Distribution Application form by: 1) Clearly marking all mandatory fields with an asterisk or placeholder text before submission, 2) Providing a summary required upfront prevent partial submissions, and 3) Implementing client-side validation highlight missing fields
simultaneously rather than sequentially.
Scoresbot = 5, S,Score =3
Assistant: I'll enhance the distribution form with better validation and field requirements. Here are the improvements: 1. Add asterisks to required fields 2. Implement client-side validation 3. Show all validation errors at once 4, Add a summary of
(More Rounds Omitted)
Figure 8: Example of WebGen-Agent trajectory.
28

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Published as a conference paper at ICLR 2026
System Prompt: You are Bolt, an expert Al assistant and exceptional senior software developer with vast knowledge across multiple programming languages, frameworks, and best practices. <system_consttrn consttrinnttnnns You are operating environment called WebContainer ....
User: Please implement multi-company dashboard for managing and displaying financial data from multiple companies. The dashboard should be able callect and display financial information from each company, provide consolidated reports, and support cross- cosmpnyy comparisons and reporting. Users should be able to browse financial data from each company, view consolidated reports, and perform financial management and reporting. Apply mint cream as the background; style all components with teal.
Assistant: I'll create a multi-company financial dashboard using React with Vite. Here's the implementation plan: 1. Set up Vite React project 2. Create components for company data display and comparison 3. Implement mack financial data storage 4. Style with mint cream background and teal components.....
Installation of dependencies was successful. Starting the service was successful. screenshot description: The screenshot shows financial dashboard with clean and organized layout. The top section features teal header with the title 'Financial Dashboard and navigation options labeled 'Dashboard' and 'Consolidated Report'. Below the header is a search bar for filtering companies The main content area is divided into four equal-sized cards ..... If no further modification is necessary, output <boltAction type= ape="sccrenshot, validated" /s to signal that the screenshot satisfactory.
<boltAction type= "screenshot validated"/>
WinDOW; down suggestions based the GUl-agent testing result: Improve navigation accessibility of cross-company comparison and financial management functionalities allow thorough testing. Ensure all critical features easily reachable and not hidden behind repetitive interactions. Consider adding more interactive elements drill-down options for detalled financial data exploration
CJiCk 121
Scoreshor 4, Scorrggi
Assistant: I'll enhance the dashboard with better comparison features and financial management tools. Here the key improvements: 1. Add comparison mode to select multiple companies 2. Include financial ratios and metrics Add export functionality for reports.....
Installation of dependencies was successful. Starting the service was successful. The screenshot description: The screenshot shows financial dashboard with clean and organized layout. The top section includes a header with the title "Financial Dashboard' and navigation options labeled 'Dashboard' and 'Consolidated Report', Below the header search bar labeled 'Search companies...' button labeled Commpree Companies' modification necessary, output <boltAction type= seee="cccennhhtt validated"// / screenshot_valldttedd"// screenshot is satisfactory.
no further
signal that the
<boltAction type= weteseeessottaalllliiiieie validated"/> />
[i]
Scroll WINDOW; down
The trajectory shows that the GUI agent successfully verified the mint cream background and teal components, browsed financial data, viewed consolidated reports, and performed cross-company comparisons. The GUI agent testing is successful and no further improvement is necessary. Observe the above feedback and decide whether further modifications the code are needed type="Finish"/
on GUl-aggent testing summary. further modification necessary, output <boltAction
Score = 4, Score
<boltAction type= "ree="finish'>
Figure 9: Example of a WebGen-Agent trajectory.
29

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Published as a conference paper at ICLR 2026
System Prompt: You Bolt, expert assistant and exceptional senior software developer with knowledge across multiple programming languages, frameworks, and best practices. <system_ constraints> You are operating In environment called WebContainer
User: Please implement dashboard website for displaying regulatory policies. The website should have data visualization capabilities to clearly display complex regulatory policies. Users should be able to browse and analyze different policies, view related data and charts, and be able to filter and sort as needed. Assign lavender to the background and indigo to the Ul elements.
Assistant: I'll create a regulatory policy dashboard with data visualization capabilities using React and Chart.js. Let me think through this step by step: 1. We need dashboard layout with: - Header with title and navigation
Installation of dependencies was successful, Starting the service was successful. The screenshot contains errors: This localhost page can't be found. Modify the code to fix the errors in screenshot.
Scoreshot 0,Score gui
Assistant: ....
Feedback: ....
Score Score Eui 0
Five Consecutive Errors, Backtrack!
Assistant:
I'll create a regulatory policy dashboard with data visualization capabilities using React and Chart.js. Let me think through this step by step: 1. We need a dashboard layout with navigation and main content area
Installation of dependencies was successful. Starting the service emitted errors: regulatory-
policy-dashboard@@.. http:///ocalhost:36533 Network: -host to expose Error: Failed scan for dependencies
VITE v4.5.14 ready in 3504 Local:
from entries: poorkspae//nddxx.hmm ERROR Expected closing "strong" tag to match opening
"p" tag prcppaees/olcccyttalllaaaasssss/ .5x33/3.......3..1.............. <p>sstrong-Effecctve D/strngt
(policy.date)</p> The opening "p" tag is here: /PolicyDetaalPaggjsss333 <strong-Effective
Date:s/strong>
Score ocorree
Assistant: I'll fix the JSX syntax errors in the PolicyDetallPage, Jsx file. The issue with mismatched HTML tags the
structure, <boltArtifact id= segulatoy-dashboard fixed" title= RRegulegglltt Policy Dashboard- Fixed"> -..
Installation of dependencies was successful. Starting the service was successful. The screenshot description: The screenshot shows Regulatory Policy Dashboard with clean and structured layout. The overall design features a purple and white color scheme, giving it professional and modern look. The dashboard includes sidebar on the left with navigation options such as Dashboard, Policies, Analytics, Reports, Settings, Filters, Categories, Status, ..... If no further modification necessary, output <boltAction ape=="screenshot.o pareenshot_vllidaeedd validatedd / to signal that the screenshot satisfactory.
Scot 4,5co gui
Assistant: Looking at the screenshot description, I can see that the dashboard is functioning but could benefit from some improvements to better meet the requirements. The current implementation only shows basic dashboard view.......
Installation of dependencies was successful. Starting the service was successful. screenshot description: screenshot shows Regulatory Policy Dashboard with clean and organized layout. The overall design features purple and white color scheme, giving professional and modern look. The dashboard includes sidebar on the left with navigation options such as Dashboard, Policies, If no further modification necessary, output <boltAction type= screenshstn validated"/> /> to signal that the screenshot is satisfactory.
<boltAction type= screenshot validated" />
WINDOW down Looking at this GUl-agent testing trajectory, can see that the agent successfully navigated through the application, verified the requested visual elements (lavender background and indigo UI elements), and performed the required actions of
Click 11
ANSWER: YES
browsing policies, using filters, and analyzing data. The GUI agent testing is successful and no further improvement is necessary. If no further modification is necessary, output <boltAction "fpis"fisss"/>> <boltAction type="finish"
Score 5, S,Score
Figure 10: Example of a WebGen-Agent trajectory.
30

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Published as a conference paper at ICLR 2026
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Pytte det WinSerdon 1t Mieeden Jerch Oven Codee-1000--
Original GOOE Accurse
Instruction
Screenshot
Figure 11: Manual testing interface of the accuracy of the screenshot scores.
Screenshot Score Accuracy Evaluation Guidelines:
Check the VLMM-generated screenshot scores each step in the WehGen-Agent trajectories. If the the does match the
match the appearance of the screenshot, select "Accurate". appearance the screenshot, select "Not accurate", a number input box would appear next to "Not accurate" choice. You should enter the accurate score in it.
Grading Scale:
(Blank Page): The screenshot is completely blank does not contain any visible content. may only have background color or display an error message.
(Poor): Major rendering issues (e.g.. broken layouts, incorrect colors). Content is irrelevant missing, Layout is chaotic. Design is outdated visually unappealing.
(Below Average): Partial rendering with noticcable errors. Content is partially relevant but poorly organized. Layout lacks consistency. Design basic or uninspired.
(Average): Mostly rendered correctly with minor flaws. Content is relevant but lacks polish. Layout is functional but unremarkable. Design is clean but lacks modern flair.
(Good): Rendered well with no major errors. Content is relevant and logically organized. Layout is harmonious and user-friee friendli.n Design is modern and visually appealing.
(Excellent): Flawless rendering. Content is highly relevant, intuitive, and tailored to user needs. Layout is polished, responsive, and innovative. Design is autting-edge. beautiful, and memorable.
Figure 12: Grading guidelines for manual evaluation of the accuracy of the screenshot scores.
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Published as a conference paper at ICLR 2026
Ossevation (4 liattd coweere Gienn Usen
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Figure 13: Manual testing interface of the accuracy of the GUI-agent scores.
GUI-agent Score Accuracy Evaluation Guidelines:
Check the "LM-ggnerated GUI-agent scores at each step the WebGen-Agent trajectorics. The GUI agent trajectories are to the right, and you can scroll to inspect them. If the score match the GUI agent trajectory, select "Accurate". If the score does not match the GUI agent trajectory, select "Not accurate", and number input box would appear to the "Not accurate" choice. You should enter the accurate score in it.
Grading Scale:
1: The vast majority of tested functions fail or behave incorrectly.
2: Many functions fail; only a few behave as expected.
3: About half the functions work as expected: success is mixed.
4: Most functions work expected; only minor issues remain.
5: All tested functions work exactly expected; no issues observed.
Figure 14: Grading guidelines for manual evaluation of the accuracy of the GUl-agent scores.
32

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Published as a conference paper at ICLR 2026
wiliter
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catieled
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to th the smpirent cake war..
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Figure 15: Manual testing interface of the comprehensiveness of the GUI-agent testing instructions.
GUI-agent Instruction Evaluation Guidelines:
Score the instruction 5, where 5 best. The dominant criterion is comprehensivenes:: how
completely the instruction translates every website requirement into concrete GUI-agent checks.
Grading Scale:
(Minimal, < 25 %)) The instruction overlooks most of the stated requirements.
(Low, 25 50 %)) Only some primary requirements are mentioned; many important items are absent.
(Moderate, 50-75 %): Core functionalities are covered, hut several secondary features or style rules are skipped.
(High, %): major functional requirements plus most visual or secondary ones are included; only a few minor details are missing.
(Complete, Rs 100 of requirements): Every requirement turned into checks. Nothing significant left out.
Figure 16: Grading guidelines for manually evaluating GUI-agent testing instructions
33

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Published as a conference paper at ICLR 2026
Accuracy and Appearance Score vs. Max Iter
Exceed Rate vs. Max Iter
Exceed Rate (1)
80
3.8
Exceed Rate (96
Accuracy (86)
3.6
60
46
3.4
44
-32
20
42
Accuracy (%)
Appearance
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
(a) Accuracy (%) and Appearance Score as a function of the maximum number of iterations.
(b) Exceed Rate (%) versus the maximum number of iterations.
Figure 17: Effect of the maximum iteration number hyper-parameter on different performance met- rics. The coding LLM used is DeepSeek-V3. V3.
34

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Published as a conference paper at ICLR 2026
Qween2.5-Coder-B-Blnstrrr (Original -> SFT >> Step-GRPO)
Putau Tommon
Dexietbll Datting Anslytica
Backefiall Asallytic
Thes leadboo
Briling Amciples
Original
SFT
Step-GRPO
Gvedit Reper Compary
Repair Servicas
Noad SANCE
Original
SFT
Step-GRPO
Original
SFT
Step-GRPO
Wedeen Suftware Selution be Yeur Rucinw
Welcome Our Software Solutions
Welcoms Software Salusions
Original
SFT
Step-GRPO
Velcome Iraunance Automaation Systems
Dareh
Original
SFT
Step-GRPO
Figure 18: Screenshots of websites created by Qwven25--Coler77BB-nntteee WebGenAggen-LM-77B SFT, and WebGenAAgett-LM- LMM-B-Step-GRPP.
35

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Published as a conference paper at ICLR 2026
Qween2.5-Coeer-7B-nnttte (Original ~> SFT >> Step-GRPO)
Welcome to Leadership & Business Training
Redcornc Our Corodlingg Siannss
Original
Step-GRPO
Trading Opportunttics
Original
SFT
Step-GRPO
Welcome 6-Goesssrent Systemn
q ginal
SFT
Step-GRPO
Lagge
Internship
Brsaes Putano
Original
SFT
Step-GRPO
Recycle
Loadlit
Sioe
Mini"
Original
SFT
Step-GRPO
Figure 19: Screenshots of websites created by Qween2.5-Codeer-BB-Innnn WebGenAggen-LM--7B. SFT, and WebGenAgent-LMN Mereggg-et---------
36

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Published as a conference paper at ICLR 2026
Qwen3-8B (Original >> SFT -> Step-GRPO)
Finncie Daabboed
Finanedd Deahbourd
Sryeet
Original
Step-GRPO
Welcome Our Ciinccl Office
Our VidA
Original
SFT
Step-GRPO
Balibard
recurree Mongrrent snpereeet Eyster
Original
SFT
Step-GRPO
Forum Wehpagee
Fenur Horee
CSCO
Original
SFT
Step-GRPO
Welcome to The Allän Bourbon Bar
Fridec
Monilyy
Original
SFT
Step-GRPO
Figure 20: Screenshots of websites created by Qwen3-8B, WebGennAgem-LM.BB--STTT and WeGGmnggeLLL.BBB-tttte...G
37

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Published as a conference paper at ICLR 2026
Qwen3-8B (Original >> SFT -> Step-GRPO)
Welcome ProredStare
This Moldbod sagr centt
Original
Step-GRPO
Stock Repart Generstor
Report
Report
Search SICCE Introaation
Original
SFT
Step-GRPO
Trand Soiki
Welcome Travel Booking
Tellet
Original
SFT
Step-GRPO
Fand
Welcome to Food Distribttinn Network
Derck
Original
SFT
Step-GRPO
Poary Ca Bection
130 benih
Original
SFT
Step-GRPO
Figure 21: Screenshots of websites created by Qwen3-8B, WebGennAgem-L--SB-STTT and VebGennggem-LM--B--eee---C SRPP
38

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Published as a conference paper at ICLR 2026
Wheel of Fortune
Spm
My
Discount
an
Disccunn
Discount
Discount
Appetizer
Spin the Wheel!
Suggestions: 1. Consider adding hover effects to the buttons to provide visual feedback when users interact with them. 2. Ensure consistent spacing around the 'Spin the Wheel!' button to maintain alignment with the top navigation buttons.....
I'll enhance the UI based on the feedback while maintaining the required functionality. Here are the improvements: <boltArtifact of Fortune Website">> <boltAction type="file" wstppaappsss///p.ssssss..s (\n max-width: 800px;An margin: 0 auto;An padding: 20px:\n text-align: center;An
title="Enhanced Wheel
Wheel of Fortune
My
15% Discount
Figure 22: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based screenshot feedback. The step simplified due to space constraints.
39

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Published as a conference paper at ICLR 2026
Home
Teams
Players
Simulation
Baseball Simulation
Manage your team, track player stats, and simulate games!
Suggestions: 1. Add more content or interactive elements to fill the blank space and provide users with immediate engagement. Consider adding icons or images related to baseball to enhance visual appeal and reinforce the theme. Implement a responsive design to
I'll enhance the home page with more engaging content and visual elements while maintaining id="baseball-
the requested color scheme. Here are the approvementss/nnnnootttttii simulation-enhancemnnss title="Enhanced Home Page">\n <boltAction type="file" filePath="src/paaeee haaph//sssc//gee////////s///s/aa Link ) from 'react-router-dom') \n
Home
Teams
Players
Simulation
Baseball Simulation Manager
Take control your team's destiny manage, strategize, and dominate!
Team Management
Player Stats
Build your manage your lineup
Track performance player development
Manage
Players
Game Simulation
Run realistic game simulations with detailed results
Simulate
Figure 23: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based screenshot feedback. The step simplified due to space constraints.
40

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Published as a conference paper at ICLR 2026
Home About Us Services
Contact Us
Welcome to Our Clinical Office
We provide comprehensive heallhcare services with patient- centered approach.
Our team of expenenced professionals dedicated your well-being.
Office Hours
Monday Friday: 8:00 AM 5:00 PM
Seturday: 9:00 AM 1:00 PM
Sunday: Closed
Suggestions: Consider adding some visual elements such as icons or images related to healthcare to enhance engagement. Additionally, introducing footer with contact information social media links could improve user experience.....
I'll enhance the website by adding footer with contact info and some healthcare icons improve visual appeal. Here are the modifications: wtwp:a/snnnnnnnnnnttnniaaaaaa id=) -deiiiiicclll-ffcc--- website-enhanced\ title=)" Clinical Office Website\">> (">|n <boltAction type=\"file\""
tlePath:\ssrc/indexxcs// > (nn margin: OAAn.....
Home
About Us
Services
Contact Us
Welcome to Our Clinical Office
We provide comprehensive healthcare services with patient- centered approach.
Our team experienced professionals dedicated well-being.
Office Hours
Monday Friday: 8:00
Seturday:
Sunday:
123 Medical Center Drive
(565) 423-4567
info@@linicalofie com
Suite 100
Mon-Frt: 8AM-5PM
Emergency: (555) 987-6543
Figure 24: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based screenshot feedback. The step simplified due to space constraints.
41

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Published as a conference paper at ICLR 2026
Home
Plans
3
Support
Account
Welcome to Our Phone Operator Service
Brawse our plans, support, menage account.
Suggestions: The layout is already quite clean and functional, but here are some suggestions for further improvement:In1. Add more visual elements such as icons or images to enhance engagement. \n2. Consider adding footer with additional links contact information
I'll enhance the website based on the feedback by adding footer, improving visual elements, and adding call-to-action button. Here the sallfiacttios/nnlnmoottttttiiiit id="phone- operator-websiteeenhaneee" title="Enhanced Phone Operator Website">\n <boltAction type="file"
(An margin: n
Home
Plans
Support
Account
Welcome to Our Phone Operator Service
Browse our plans, get support, manage your account.
View Our Plans
11-800-1234455
Phone Operator. rights reserved.
Figure 25: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based screenshot feedback. The step simplified due to space constraints.
42

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Published as a conference paper at ICLR 2026
T
Custom Shirts
Y Customnion Ronder
Customize Your Shirt Shirt Model
Classic Fi- Traditional relaxed fit
breathable
Size
M
Color
Suggestions: 1. Replace the placeholder black lines under 'Color' with actual color swatches or options for better user interaction. Add spacing between sections (e.g., between 'Size' and 'Color') to improve readability and visual separation. Consider adding preview
see from the screenshot that the color picker showing as black lines instead of proper color swatches, and the layout needs some improvements. Let fix these issues: (ouues:\nnn<bbotArttiiee atd=:siirccsttmiatiiioeeemmmm Customization System" IncbolltAccioon type= "file" filePath=.....
title="E-commen Shirt
Y
Custom Shirts
Customize
Order
Customize Your Shirt
Preview
Shirt Model
Classic Fit
Fabric
Cotton Soft and breathable
Size
M
Price Summary
Base Price:
$29.99
Color
Customization Fee:
$5.00
Figure 26: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based screenshot feedback. The step simplified due to space constraints.
43

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Published as a conference paper at ICLR 2026
Search Results
Technical Solutions Hub
Everything You Need
Type [5]; "technical articles"
Type [5]; "technical solutions
Search Results
Search Results
Type [5]; "technology"
Click
Suggestions: 1. Fix the search functionality to properly index and return relevant technical
content, or populate the database with searchable articles and solutions. 2. Implement proper error messaging for failed searches and form submissions provide user feedback
need to address the specific issues mentioned in the feedback. Let me fix the search functionality, form submission issues, and add proper error messaging. \n\n<boltArtifact (d-"technical-weesitee Iithh=lTechnical Information and Solutions Website"> AncboltAttono type==
Tuchilib
Comeerti
Techhuk
Search Results
Technical Solutions Hub
Everything You Need
NA sreutt Swand
Type [5]: "technical articles"
Type [5]; "solutions"
Teehhuch
Teehhuh
Search Results
Debug: CSS Layout Issues
Cetean ESS Levees lasoed pou
Problees
Salution
Click [11]
Click [6]
Figure 27: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based GUI-agent testing feedback. The step is simplified due to space constraints.
44

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Published as a conference paper at ICLR 2026
Winpled Sysscon
Ha Deahbruudd
Preenial Management
50
Halry
Click
Click
Hanagamant
Hanagemart
Finandaal Manngee
Finnndal Nanager
50
100
Type [1]; Patient Fee Collection: Type [2]; 500; Click [4]
The transaction submission functionality is not working properly. This prevents users from recording financial data and blocks testing of other dependent features. The system should ensure that the transaction is properly
Suggestions:
need to debug and fix the transaction submission issue. Let me add better error handling and ensure the transaction data is properly saved and displayed. InwnsbottArtiifctt id= id="hospital- management-system' title="Hospital Management System"> \nsbblltActioonn type="file" filePath= hwwp:///mm/etttii/aaaaaaaaaaeeee i>mporrt React
Harighaa Dhoilouadd
Froncia Masagesntt
+
$0
$0
50
monoseee ranry
Click 22]
Click [11
Firundal Managan
Frandia Nasagement
50
SO
$150
50
$150
Hivrrry
Type [1]; Patient Fee Collection; Type [2]; 150; Click [4].....
Click [9]
Figure 28: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based GUI-agent testing feedback. The step is simplified due to space constraints.
45

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Published as a conference paper at ICLR 2026
Govermment
Government
Type [O]; admin; Type [1]; admin123456; Click
Type (0]; admin; Type [1]; admin123456;
Government
Govermment
System
Type [o]; admin; Type [1]; admin123456; Click [2]
Type [O]; admin; Type [1]; admin123456; Click .....
Suggestions: The login form has a critical issue where the password field fails to register or retain entered values, consistently showing 'password field empty' errors despite multiple input attempts. This suggests problems with form input handling, data binding
need to fix the login form issue where the password field not properly registering input values. Let update the Login component to ensure proper form handling and validation. \n) \n\n<boltArttffat id=" e-gooerrnmeetssstteee" title="E-Government Office Management System"> lsncbolttcttinooinnnnnnnno type= "file" filePath=
Government Manapement System
Govemment
System
Dashieent Denneew
ft
Type [O]: admin; Type [1]; admin123456; Click (2)
Click [1]
Covernment Management System
E-Gover
Office Management
Oftic Masagen
Click (8)
Click [0]
Figure 29: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based GUI-agent testing feedback. The step is simplified due to space constraints.
46

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Published as a conference paper at ICLR 2026
LinkTree Manager Coail
LinkTree Manager
Coadi proadled
Link Time curu
Your
Click [3]
Click
LinkTree Manager
LinkTree Manager
Cren percention
Your
Your
Click [3]
Click
The 'Add New Link' button appears to be completely non-functional, as evidenced by consecutive clicks with response or state change. This a critical issue that prevents from accessing the core functionality of the LinkTree Manager .......
Suggestions:
need to fix the non-functional "Add New Link" button. Let me debug and update the code hwtr:o/niityy.mnooooootttttttttt "dd"linktree-webbstt" title="Link Tree Website with CRUD Operations ccnbitttiiiiiii type="file" filePath=" fllePath="rrccoompoeeee o import React, { useState from 'react'
ensure proper
LinkTree Manager
LinkTree Manager
Link
your mnciier
Your Links (0)
Click [3]
Type [1]; Papaya Whip Background
LinkTree Manager
LinkTrea Manager
Your Links (1)
Type [2]; http:p///aammeeecoom/////////ppp
Click [2)
Figure 30: Example of a step in WebGen-Agent trajectory where the agent improves the website's appearance based GUI-agent testing feedback. The step is simplified due to space constraints.
47

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Published as a conference paper at ICLR 2026
TWise Wear
Available Contests
Available Contests
Generd Knoe
science
Generaal Krondedge
Click [3]
Click [2]
Saiee wear
Ineu Uncasssc
Leaderboard
Available Contests
Gorseral Krooddadge
Sclence
History Chaillnge
Click [1]
Click [2] .....
Suggestions: 1. Ensure contest questions are accessible after enrollment, either by redirecting users to the questions automatically or providing a clear navigation path (e.g., a 'Start Contest' button). 2. Add a dedicated section or page for answering questions and submitting
I'll enhance the app by adding contest access after enrollment and improving the question flow. Here are the wefffiaations/nnnnnolttttttee Contest Enhancements" >nn <boltAction type="file" jotpa//ss////agee////s/ssssaaa/
ttttiiiacontetteeeaaceeeentt Trivaa
useState } from 'react'
Available Contests
Kroweeedge
Scnmnce Quiz
Matory Challege
Click [3]
Click [4]
which
Click [7]
Click [7]
Figure 31: Example of a step in a WebGen-Agent trajectory where the agent improves the website's appearance based on GUI-agent testing feedback. The step is simplified due to space constraints.
48


Reviews:
[{"review_id": "z19d7LoL07", "reviewer": "Reviewer_mw4M", "summary": "This paper presents WebGen-Agent, a test-time inference framework that leverages screenshots and GUI-agent feedback signals to iteratively improve the appearance and performance of web generation from scratch. The authors demonstrate that using WebGen-Agent leads to substantial gains for both proprietary and open-source models compared to prior frameworks such as Bolt.diy. They also introduce Step-GRPO, which employs rewards derived from feedback based on screenshots and GUI-agent evaluations. The models Qwen2.5-Coder-7B-Instruct and Qwen3-8B are trained with SFT and Step-GRPO, achieving notable improvements over their base models.", "questions": "Please address the issues raised in weaknesses section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Strong ablation studies demonstrating the contribution of each component, including different rewards, backtracking, and the impact of SFT.\n\n- Significant improvements shown with both WebGen-Agent and step-GRPO.\n\n- Excellent reproducibility, with detailed appendices that include all prompts, sample outputs, and qualitative examples.", "weaknesses": "- The source of instructions for the 700 trajectories used in SFT training (generated with DeepSeek-V3 using WebGen-Agent) is not specified. The authors should clarify whether the data originate from WebGen-Instruct or WebGen-Bench. This is a critical point not clarified in the paper to ensure there is no data leakage.\n\n- The paper does not provide a cost or time comparison between WebGen-Agent and Bolt.diy. For proprietary models, it would be helpful to include runtime or cost data, while for open-source models, a FLOPs or time comparison would clarify efficiency.\n\n- The results do not include performance for WebGen-LM-3B, 7B, and 32B with WebGen-Agent. Including these models would provide a more comprehensive comparison, especially since WebGenAgent-7B and WebGen-LM-7B share the same base model (Qwen2.5-Coder-Instruct-7B).\n\n- It would be beneficial to determine which model acts as the most reliable judge or produces the most aligned outputs. Benchmarks such as PairBench [1] or AgentRewardBench [2] could help evaluate this aspect.\n\n---\nMinor Comments\n\n- The formula fonts in Figure 2 should be larger for improved readability.\n\n[1] PairBench: Are Vision-Language Models Reliable at Comparing What They See?\n\n[2] AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5ZLJhOjPai", "reviewer": "Reviewer_z3Vc", "summary": "This paper focuses on the creation of webpages with LLM. In particular, it uses a recent benchmark  (`WebGen-Bench`, from [Lu et al 2025](https://arxiv.org/abs/2505.03733)) to develop an agentic scaffold tailored to it, and benchmarks a variety of models with that scaffolding.\n\nOn top of that, the paper proposes to finetune smaller (7B) models with data distillation through SFT and a GRPO-like loss for RL showing significant gains", "questions": "* From Table 4 it seems that the VLM used as judge does not necessarily have a major impact. Did you benchmark this behaviour when finetuning the smaller models with GRPO?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The resulting benchmark numbers are impressive. The evaluation benchmark is quite novel, but the gains are still significant\n\n* The use of the reward signal in an RL setting is interesting by itself, and a good use-case of using VLM-guided models-as-a-judge evaluation as reward", "weaknesses": "* Most of the paper is dedicated to the agentic scaffolding, which I consider to be a minor contribution and that by itself alone is below the significance criteria for an ICLR paper\n\n* Only one benchmark is used, and therefore it is not clear if the scaffolding or the general proposal generalizes to other datasets\n\n* The final numbers of the 7B models are impressive, however most of the gains are obtained from the SFT step which consists in data-distilling Deepseek. The use of a large VLM model as judge also raises the question if the gains are not \"just\" distillation from a larger models, or if there is some learning that could generalize to larger models", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8RzhGncSjW", "reviewer": "Reviewer_jD87", "summary": "This work proposes WebGen-Agent, which augments a website coding agent with two automatic evaluators: a VLM that scores a landing‑page screenshot for visual quality, and a GUI agent that exercises the site and grades functionality. These signals are used in two modes: (i) inference‑time self‑revision for closed‑source LLMs (iteratively editing the codebase), and (ii) training for open models GRPO at every editing iteration of the codebase. The author compares WebGen-Agent with existing general-purpose coding agents on the WebGen-Bench dataset. The results suggest that the proposed method outperforms competing methods.", "questions": "Questions:\n\n* How did you implement the OpenHands baseline?\n* What is the exact definition of Accuracy in Table 3? Is it computed from Yes/Partial/No/Start Failed?\n* Why does the experiment only adopt one dataset? There are other existing website coding benchmark such as Web2Code and Design2Code.\n\nTypo: \n1. L235: Wrong inline citation format\n2. L134: is used to reflect**s**\n3. L1044: The definition of the scores are presented (subject-verb agreement)", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It is a novel and interesting idea that combines VLM‑based visual feedback with a GUI agent for functional feedback, moving beyond execution‑only signals.\n\n2. On WebGen‑Bench, the method significantly outperforms general‑purpose coding agents that lack VLM/GUI feedback, indicating the value of integrating visual and functional signals.\n\n3. The author conducts experiments with various open and proprietary LLMs, as well as thorough ablation studies.", "weaknesses": "1. The VLM feedback is based on a single-page screenshot, which ignores the coherence and consistency across multiple pages.\n2. The reliability of the functionality reward is bottlenecked by the performance of the GUI agent. However, this remains a challenging and open problem, especially when the website is complex.\n3. Some experiment details are missing and confusing. For instance, the author doesn't mention how the human annotation is conducted. Also, OpenHands is an open platform; it's unclear how it is included as a baseline. \n4. While the proposed method is novel in the area of web coding agents, similar ideas have been well explored in a broader field: using external LLM-as-judge and/or agent-as-judge to provide reward or natural language feedback, fine-tuning the LLM with an existing RL algorithm.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "H9Cweii2ym", "reviewer": "Reviewer_Cbyh", "summary": "This paper introduces WebGen-Agent, a framework for website code generation that jointly leverages execution-based functional feedback and visual feedback to iteratively refine website code.\nIn addition, the authors propose Step-GRPO, which uses automated functional and visual feedback from WebGen-Agent as step-wise reward signals.\nExperimental results on WebGen-Bench validate that both WebGen-Agent and Step-GRPO meaningfully improve functional accuracy and appearance scores.", "questions": "Some of suggestions: \n- Please include the missing details of the human evaluation process.\n\n- Please clarify why the execution-only baseline already outperforms Bolt.diy.\n\n- Please provide more detailed distinctions between WebGen-Agent and related works such as Aider and Bolt.diy.\n\n- Please clarify which backbone is used in every Figure and Table. \n\n- Since Step-GRPO uses training-time feedback, the reward quality could vary depending on whether the test cases are gold or model-generated. If gold test cases exist in WebGen-Instruct, it would be insightful to compare benchmark performance when using gold vs. generated test cases as reward signals.\n\n- A recent work, namely TDDev [1], achieves 78.2 / 70.2 accuracy with GPT-4.1 / Claude-4 backbones on WebGen-Bench. It would be interesting to check whether WebGen-Agent scales with TDDev or to compare WebGen-Agent with TDDev under the same backbone (e.g., Qwen3-Coder-30B-A3B-Inst.).\n\n[1] Wan, Yuxuan, et al. \"Automatically Generating Web Applications from Requirements via Multi-Agent Test-Driven Development.\" arXiv preprint arXiv:2509.25297 (2025).", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 4, "strengths": "- Extensive experimental evaluation.\n\n- Proposes both training-free (WebGen-Agent) and training-based (Step-GRPO) approaches.", "weaknesses": "- Human annotation transparency (Appendix E, Tables 5 and 6): Details of human evaluation are missing, including the number of annotators, recruitment method, compensation, annotation interface, and inter-annotator agreement (e.g., Cohen's kappa).\n\n- Execution-only already beats Bolt.diy: The execution-only baseline in the table already surpasses Bolt.diy’s reported accuracy, yet the authors do not clarify why their setting performs better even before applying the proposed method. There might be potential mismatches in iteration counts or evaluation conditions (e.g., 20 iterations for Bolt.diy).\n\n- Relation to prior work (Bolt.diy): The difference is only briefly mentioned (\"similar to our work\" in line 452), but the precise factors behind the performance gap (e.g., reward type, iteration loop design, or evaluation feedback fidelity) are not explained.\n\n- Ambiguity in backbone settings: Some reports (e.g., Table 2, Figure 12) omit which LLM backbone was used, complicating interpretation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)