Update pipeline tag, add paper ID, abstract, and GitHub link

README.md

@@ -1,7 +1,8 @@
 ---
 base_model: Qwen/Qwen2.5-VL-7B-Instruct
 library_name: peft
-pipeline_tag: text-generation
+pipeline_tag: image-text-to-text
+paper: 2509.23909
 tags:
 - base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct
 - lora
@@ -29,6 +30,16 @@ tags:
 </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
+[EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling](https://huggingface.co/papers/2509.23909)
+
+### 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.
+
+## Code Repository
+The official code can be found on GitHub: [https://github.com/VectorSpaceLab/EditScore](https://github.com/VectorSpaceLab/EditScore)
+
 ## ✨ 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.
@@ -165,4 +176,4 @@ If you find this repository or our work useful, please consider giving a star
   journal={arXiv preprint arXiv:2509.23909},
   year={2025}
 }
-```
+```