Improve dataset card: Add metadata, abstract, and GitHub link

This PR enhances the `EditReward-Bench` dataset card by:
- Adding `task_categories: ['image-text-to-text']`, `language: ['en']`, and relevant `tags` to the metadata for improved discoverability.
- Including the paper's abstract in a dedicated section to provide comprehensive background information.
- Adding an explicit GitHub badge (`https://github.com/VectorSpaceLab/EditScore`) to the top section for easier access to the associated code.
- Ensuring the existing "Quick Start" section, which includes a sample usage code snippet, remains prominently displayed.

README.md

@@ -1,5 +1,16 @@
 ---
 license: apache-2.0
+language:
+- en
+task_categories:
+- image-text-to-text
+tags:
+- image-editing
+- reward-modeling
+- reinforcement-learning
+- benchmark
+- evaluation
+- multimodal
 ---
 
 <p align="center">
@@ -9,6 +20,7 @@ license: apache-2.0
 <p align="center">
   <a href="https://vectorspacelab.github.io/EditScore"><img src="https://img.shields.io/badge/Project%20Page-EditScore-yellow" alt="project page"></a>
   <a href="https://arxiv.org/abs/2509.23909"><img src="https://img.shields.io/badge/arXiv%20paper-2509.23909-b31b1b.svg" alt="arxiv"></a>
+  <a href="https://github.com/VectorSpaceLab/EditScore"><img src="https://img.shields.io/badge/GitHub-Code-blue" alt="github code"></a>
   <a href="https://huggingface.co/collections/EditScore/editscore-68d8e27ee676981221db3cfe"><img src="https://img.shields.io/badge/EditScore-🤗-yellow" alt="model"></a>
   <a href="https://huggingface.co/datasets/EditScore/EditReward-Bench"><img src="https://img.shields.io/badge/EditReward--Bench-🤗-yellow" alt="dataset"></a>
 </p>
@@ -23,6 +35,11 @@ license: apache-2.0
 </h4>
 
 **EditScore** is a series of state-of-the-art open-source reward models (7B–72B) designed to evaluate and enhance instruction-guided image editing.
+
+## Paper Abstract
+
+Instruction-guided image editing has achieved remarkable progress, yet current models still face challenges with complex instructions and often require multiple samples to produce a desired result. Reinforcement Learning (RL) offers a promising solution, but its adoption in image editing has been severely hindered by the lack of a high-fidelity, efficient reward signal. In this work, we present a comprehensive methodology to overcome this barrier, centered on the development of a state-of-the-art, specialized reward model. We first introduce EditReward-Bench, a comprehensive benchmark to systematically evaluate reward models on editing quality. Building on this benchmark, we develop EditScore, a series of reward models (7B-72B) for evaluating the quality of instruction-guided image editing. Through meticulous data curation and filtering, EditScore effectively matches the performance of learning proprietary VLMs. Furthermore, coupled with an effective self-ensemble strategy tailored for the generative nature of EditScore, our largest variant even surpasses GPT-5 in the benchmark. We then demonstrate that a high-fidelity reward model is the key to unlocking online RL for image editing. Our experiments show that, while even the largest open-source VLMs fail to provide an effective learning signal, EditScore enables efficient and robust policy optimization. Applying our framework to a strong base model, OmniGen2, results in a final model that shows a substantial and consistent performance uplift. Overall, this work provides the first systematic path from benchmarking to reward modeling to RL training in image editing, showing that a high-fidelity, domain-specialized reward model is the key to unlocking the full potential of RL in this domain.
+
 ## ✨ Highlights
 - **State-of-the-Art Performance**: Effectively matches the performance of leading proprietary VLMs. With a self-ensembling strategy, **our largest model surpasses even GPT-5** on our comprehensive benchmark, **EditReward-Bench**.
 - **A Reliable Evaluation Standard**: We introduce **EditReward-Bench**, the first public benchmark specifically designed for evaluating reward models in image editing, featuring 13 subtasks, 11 state-of-the-art editing models (*including proprietary models*) and expert human annotations.