Update README.md

README.md

@@ -1,199 +1,212 @@
-<p align="center">
-    <img src="./docs/assets/logo.svg" alt="Logo" width="120" />
-    <p align="center">
-        <a href="https://github.com/PKU-DAIR">
-            <img alt="Static Badge" src="https://img.shields.io/badge/%C2%A9-PKU--DAIR-%230e529d?labelColor=%23003985">
-        </a>
-    </p>
-</p>
-
-## **WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning**
-
-[Paper](https://arxiv.org/pdf/2510.04097) | [中文](./docs/Chinese.md)
-
-## **🔍 Overview**
-
-**WebRenderBench** is a large-scale benchmark designed to advance **WebUI-to-Code** research for multimodal large language models (MLLMs) through evaluation on real-world webpages. It provides:
-
-* **45,100** real webpages collected from public portal websites
-* **High diversity and complexity**, covering a wide range of industries and design styles
-* **Novel evaluation metrics** that quantify **layout and style consistency** based on rendered pages
-* The **ALISA reinforcement learning framework**, which uses the new metrics as reward signals to optimize generation quality
-
----
-
-## **🚀 Key Features**
-
-### **Beyond the Limitations of Traditional Benchmarks**
-
-WebRenderBench addresses the core issues of existing WebUI-to-Code benchmarks in data quality and evaluation methodology:
-
-| Aspect                     | Traditional Benchmarks                                                                          | Advantages of WebRenderBench                                                                 |
-| :------------------------- | :---------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------- |
-| **Data Quality**           | Small-scale, simple-structured, or LLM-synthesized data with limited diversity                  | Large-scale, real-world, and structurally complex webpages that present higher challenges    |
-| **Evaluation Reliability** | Relies on visual APIs (high cost) or code-structure comparison (fails to handle code asymmetry) | Objectively and efficiently evaluates layout and style consistency based on rendered results |
-| **Training Effectiveness** | Difficult to optimize on crawled data with asymmetric code structures                           | Proposed metrics can be directly used as RL reward signals to enhance model optimization     |
-
----
-
-### **Dataset Characteristics**
-
-<p align="center">  
-<img src="./docs/assets/framework.svg" alt="WebRenderBench and ALISA Framework" width="80%" />  
-</p>  
-<p align="center"><i>Figure 1: Dataset construction pipeline and the ALISA framework</i></p>  
-
-Our dataset is constructed through a systematic process to ensure both **high quality** and **diversity**:
-
-1. **Data Collection**: URLs are obtained from open enterprise portal datasets. A high-concurrency crawler captures 210K webpages along with static resources.
-2. **Data Processing**: MHTML pages are converted into HTML files, and cross-domain resources are processed to ensure local renderability and full-page screenshots.
-3. **Data Cleaning**: Pages with abnormal sizes, rendering errors, or missing styles are filtered out. Multimodal QA models further remove low-quality samples with large blank areas or overlapping elements, yielding 110K valid pages.
-4. **Data Categorization**: Pages are categorized by industry and complexity (measured via *Group Count*) to ensure balanced distribution across difficulty levels and domains.
-
-Finally, we construct a dataset of **45.1K** samples, evenly split into training and test sets.
-
----
-
-## **🌟 Evaluation Framework**
-
-We propose a novel evaluation protocol based on **rendered webpages**, quantifying model performance along two key dimensions: **layout** and **style consistency**.
-
----
-
-### **RDA (Relative Layout Difference of Associated Elements)**
-
-**Purpose:** Measures relative layout differences between matched elements.
-
-* **Element Association:** Matches corresponding elements between generated and target pages using text similarity (LCS) and geometric distance.
-* **Positional Deviation:** The page is divided into a 3×3 grid. Associated elements are compared quadrant-wise—if located in different quadrants, the score is 0; otherwise, a deviation-based score is computed.
-* **Uniqueness Weighting:** Each element is weighted by its uniqueness (inverse group size), giving higher importance to distinctive components.
-
----
-
-### **GDA (Group-wise Difference in Element Counts)**
-
-**Purpose:** Measures group-level alignment of axis-aligned elements.
-
-* **Grouping:** Elements aligned on the same horizontal or vertical axis are treated as one group.
-* **Count Comparison:** Compares whether corresponding groups in the generated and target pages contain the same number of elements.
-* **Uniqueness Weighting:** Weighted by element uniqueness to emphasize key structural alignment.
-
----
-
-### **SDA (Style Difference of Associated Elements)**
-
-**Purpose:** Evaluates fine-grained style differences between associated elements.
-
-* **Multi-Dimensional Style Extraction:** Measures differences in foreground color, background color, font size, and border radius.
-* **Weighted Averaging:** Computes a weighted mean of style similarity scores across all associated elements to obtain an overall style score.
-
----
-
-## **⚙️ Installation Guide**
-
-### **Core Dependencies**
-
-<!--  
-# Recommended: Use vLLM for faster inference  
-pip install vllm transformers>=4.40.0 torch>=2.0
-
-# Other dependencies  
-pip install selenium pandas scikit-learn pillow
-
-Alternatively:
-pip install -r requirements.txt  
--->
-
-Coming Soon
-
----
-
-## **📊 Benchmark Workflow**
-
-### **Directory Structure**
-
-```
-|- docs/                     # Documentation
-|- scripts                   # Evaluation scripts  
-|- web_render_test.jsonl      # Test set metadata  
-|- web_render_train.jsonl     # Training set metadata  
-|- test_webpages.zip          # Test set webpages  
-|- train_webpages.zip         # Training set webpages  
-|- test_screenshots.zip       # Test set screenshots  
-|- train_screenshots.zip      # Training set screenshots  
-```
-
----
-
-### **Implementation Steps**
-
-1. **Data Preparation**
-
-   * Download the WebRenderBench dataset and extract webpage and screenshot archives.
-   * Each pair consists of a real webpage (HTML + resources) and its rendered screenshot.
-
-2. **Model Inference**
-
-   * Run inference using engines such as **vLLM** or **LLM Deploy**, and save results to the designated directory.
-
-3. **Evaluation**
-
-   * Run `scripts/1_get_evaluation.py`.
-   * The script launches a web server to render both generated and target HTML.
-   * WebDriver extracts DOM information and computes **RDA**, **GDA**, and **SDA** scores.
-   * Results are saved under `save_results/`.
-   * Final scores are aggregated via `scripts/2_compute_alisa_scores.py`.
-
-4. **ALISA Training (Optional)**
-
-   * Use `models/train_rl.py` for reinforcement learning fine-tuning. *(Coming Soon)*
-   * The computed evaluation scores serve as reward signals to optimize policy models via methods such as **GRPO**.
-
----
-
-## **📈 Model Performance Insights**
-
-We evaluate **17 multimodal large language models** of varying scales and architectures (both open- and closed-source).
-
-* **Combined Scores of RDA, GDA, and SDA (%)**
-
-![Inference Results](./docs/assets/inference_results.png)
-
-**Key Findings:**
-
-* Overall, larger models achieve higher consistency. **GPT-4.1-mini** and **Qwen-VL-Plus** perform best among closed-source models.
-* While most models perform reasonably on simple pages (*Group Count* < 50), **RDA scores drop sharply** as page complexity increases—precise layout alignment remains a major challenge.
-* After reinforcement learning via the **ALISA framework**, **Qwen2.5-VL-7B** shows substantial improvements across all complexity levels, even surpassing **GPT-4.1-mini** on simpler cases.
-
----
-
-## **📅 Future Work**
-
-* [ ] Release pretrained models fine-tuned with the ALISA framework
-* [ ] Expand dataset coverage to more industries and dynamic interaction patterns
-* [ ] Open-source the complete toolchain for data collection, cleaning, and evaluation
-
----
-
-## **📜 License**
-
-The **WebRenderBench dataset** is released for **research purposes only**.
-All accompanying code will be published under the **Apache License 2.0**.
-
-All webpages in the dataset are collected from publicly accessible enterprise portals.
-To protect privacy, all personal and sensitive information has been removed or modified.
-
----
-
-## **📚 Citation**
-
-If you use our dataset or framework in your research, please cite the following paper:
-
-```bibtex
-@article{webrenderbench2025,
-  title={WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning},
-  author={Anonymous Author(s)},
-  year={2025},
-  journal={arXiv preprint},
-}
-```
+---
+license: apache-2.0
+task_categories:
+- token-classification
+language:
+- en
+- zh
+tags:
+- agent
+size_categories:
+- 100K<n<1M
+---
+
+<p align="center">
+    <img src="./docs/assets/logo.svg" alt="Logo" width="120" />
+    <p align="center">
+        <a href="https://github.com/PKU-DAIR">
+            <img alt="Static Badge" src="https://img.shields.io/badge/%C2%A9-PKU--DAIR-%230e529d?labelColor=%23003985">
+        </a>
+    </p>
+</p>
+
+## **WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning**
+
+[Paper](https://arxiv.org/pdf/2510.04097) | [中文](./docs/Chinese.md)
+
+## **🔍 Overview**
+
+**WebRenderBench** is a large-scale benchmark designed to advance **WebUI-to-Code** research for multimodal large language models (MLLMs) through evaluation on real-world webpages. It provides:
+
+* **45,100** real webpages collected from public portal websites
+* **High diversity and complexity**, covering a wide range of industries and design styles
+* **Novel evaluation metrics** that quantify **layout and style consistency** based on rendered pages
+* The **ALISA reinforcement learning framework**, which uses the new metrics as reward signals to optimize generation quality
+
+---
+
+## **🚀 Key Features**
+
+### **Beyond the Limitations of Traditional Benchmarks**
+
+WebRenderBench addresses the core issues of existing WebUI-to-Code benchmarks in data quality and evaluation methodology:
+
+| Aspect                     | Traditional Benchmarks                                                                          | Advantages of WebRenderBench                                                                 |
+| :------------------------- | :---------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------- |
+| **Data Quality**           | Small-scale, simple-structured, or LLM-synthesized data with limited diversity                  | Large-scale, real-world, and structurally complex webpages that present higher challenges    |
+| **Evaluation Reliability** | Relies on visual APIs (high cost) or code-structure comparison (fails to handle code asymmetry) | Objectively and efficiently evaluates layout and style consistency based on rendered results |
+| **Training Effectiveness** | Difficult to optimize on crawled data with asymmetric code structures                           | Proposed metrics can be directly used as RL reward signals to enhance model optimization     |
+
+---
+
+### **Dataset Characteristics**
+
+<p align="center">  
+<img src="./docs/assets/framework.svg" alt="WebRenderBench and ALISA Framework" width="80%" />  
+</p>  
+<p align="center"><i>Figure 1: Dataset construction pipeline and the ALISA framework</i></p>  
+
+Our dataset is constructed through a systematic process to ensure both **high quality** and **diversity**:
+
+1. **Data Collection**: URLs are obtained from open enterprise portal datasets. A high-concurrency crawler captures 210K webpages along with static resources.
+2. **Data Processing**: MHTML pages are converted into HTML files, and cross-domain resources are processed to ensure local renderability and full-page screenshots.
+3. **Data Cleaning**: Pages with abnormal sizes, rendering errors, or missing styles are filtered out. Multimodal QA models further remove low-quality samples with large blank areas or overlapping elements, yielding 110K valid pages.
+4. **Data Categorization**: Pages are categorized by industry and complexity (measured via *Group Count*) to ensure balanced distribution across difficulty levels and domains.
+
+Finally, we construct a dataset of **45.1K** samples, evenly split into training and test sets.
+
+---
+
+## **🌟 Evaluation Framework**
+
+We propose a novel evaluation protocol based on **rendered webpages**, quantifying model performance along two key dimensions: **layout** and **style consistency**.
+
+---
+
+### **RDA (Relative Layout Difference of Associated Elements)**
+
+**Purpose:** Measures relative layout differences between matched elements.
+
+* **Element Association:** Matches corresponding elements between generated and target pages using text similarity (LCS) and geometric distance.
+* **Positional Deviation:** The page is divided into a 3×3 grid. Associated elements are compared quadrant-wise—if located in different quadrants, the score is 0; otherwise, a deviation-based score is computed.
+* **Uniqueness Weighting:** Each element is weighted by its uniqueness (inverse group size), giving higher importance to distinctive components.
+
+---
+
+### **GDA (Group-wise Difference in Element Counts)**
+
+**Purpose:** Measures group-level alignment of axis-aligned elements.
+
+* **Grouping:** Elements aligned on the same horizontal or vertical axis are treated as one group.
+* **Count Comparison:** Compares whether corresponding groups in the generated and target pages contain the same number of elements.
+* **Uniqueness Weighting:** Weighted by element uniqueness to emphasize key structural alignment.
+
+---
+
+### **SDA (Style Difference of Associated Elements)**
+
+**Purpose:** Evaluates fine-grained style differences between associated elements.
+
+* **Multi-Dimensional Style Extraction:** Measures differences in foreground color, background color, font size, and border radius.
+* **Weighted Averaging:** Computes a weighted mean of style similarity scores across all associated elements to obtain an overall style score.
+
+---
+
+## **⚙️ Installation Guide**
+
+### **Core Dependencies**
+
+<!--  
+# Recommended: Use vLLM for faster inference  
+pip install vllm transformers>=4.40.0 torch>=2.0
+
+# Other dependencies  
+pip install selenium pandas scikit-learn pillow
+
+Alternatively:
+pip install -r requirements.txt  
+-->
+
+Coming Soon
+
+---
+
+## **📊 Benchmark Workflow**
+
+### **Directory Structure**
+
+```
+|- docs/                     # Documentation
+|- scripts                   # Evaluation scripts  
+|- web_render_test.jsonl      # Test set metadata  
+|- web_render_train.jsonl     # Training set metadata  
+|- test_webpages.zip          # Test set webpages  
+|- train_webpages.zip         # Training set webpages  
+|- test_screenshots.zip       # Test set screenshots  
+|- train_screenshots.zip      # Training set screenshots  
+```
+
+---
+
+### **Implementation Steps**
+
+1. **Data Preparation**
+
+   * Download the WebRenderBench dataset and extract webpage and screenshot archives.
+   * Each pair consists of a real webpage (HTML + resources) and its rendered screenshot.
+
+2. **Model Inference**
+
+   * Run inference using engines such as **vLLM** or **LLM Deploy**, and save results to the designated directory.
+
+3. **Evaluation**
+
+   * Run `scripts/1_get_evaluation.py`.
+   * The script launches a web server to render both generated and target HTML.
+   * WebDriver extracts DOM information and computes **RDA**, **GDA**, and **SDA** scores.
+   * Results are saved under `save_results/`.
+   * Final scores are aggregated via `scripts/2_compute_alisa_scores.py`.
+
+4. **ALISA Training (Optional)**
+
+   * Use `models/train_rl.py` for reinforcement learning fine-tuning. *(Coming Soon)*
+   * The computed evaluation scores serve as reward signals to optimize policy models via methods such as **GRPO**.
+
+---
+
+## **📈 Model Performance Insights**
+
+We evaluate **17 multimodal large language models** of varying scales and architectures (both open- and closed-source).
+
+* **Combined Scores of RDA, GDA, and SDA (%)**
+
+![Inference Results](./docs/assets/inference_results.png)
+
+**Key Findings:**
+
+* Overall, larger models achieve higher consistency. **GPT-4.1-mini** and **Qwen-VL-Plus** perform best among closed-source models.
+* While most models perform reasonably on simple pages (*Group Count* < 50), **RDA scores drop sharply** as page complexity increases—precise layout alignment remains a major challenge.
+* After reinforcement learning via the **ALISA framework**, **Qwen2.5-VL-7B** shows substantial improvements across all complexity levels, even surpassing **GPT-4.1-mini** on simpler cases.
+
+---
+
+## **📅 Future Work**
+
+* [ ] Release pretrained models fine-tuned with the ALISA framework
+* [ ] Expand dataset coverage to more industries and dynamic interaction patterns
+* [ ] Open-source the complete toolchain for data collection, cleaning, and evaluation
+
+---
+
+## **📜 License**
+
+The **WebRenderBench dataset** is released for **research purposes only**.
+All accompanying code will be published under the **Apache License 2.0**.
+
+All webpages in the dataset are collected from publicly accessible enterprise portals.
+To protect privacy, all personal and sensitive information has been removed or modified.
+
+---
+
+## **📚 Citation**
+
+If you use our dataset or framework in your research, please cite the following paper:
+
+```bibtex
+@article{webrenderbench2025,
+  title={WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning},
+  author={Anonymous Author(s)},
+  year={2025},
+  journal={arXiv preprint},
+}
+```