EditReward-Data

This repository contains EditReward-Data, a large-scale, high-fidelity human preference dataset for instruction-guided image editing. It was introduced in the paper EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing.

EditReward-Data comprises over 200K manually annotated preference pairs. These annotations were meticulously curated by trained experts following a rigorous and standardized protocol, ensuring high alignment with considered human judgment and minimizing label noise. The dataset covers a diverse range of edits produced by seven state-of-the-art models across twelve distinct sources. It serves as crucial training data for reward models like EditReward, designed to score instruction-guided image edits.

• Paper: EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing
• Project Page: https://tiger-ai-lab.github.io/EditReward
• Code Repository: https://github.com/TIGER-AI-Lab/EditReward

Dataset Overview

EditReward-Data is designed to enable the training of reward models that can score instruction-guided image edits. The dataset facilitates assessing and improving the alignment of image editing models with human preferences. The dataset statistics are shown below:

Sample Usage

To download the EditReward-Data dataset to your local machine, use the huggingface-cli command:

huggingface-cli download --repo-type dataset TIGER-Lab/EditReward-Data --local-dir /your-local-dataset-path

Citation

Please kindly cite our paper if you use our code, data, models, or results:

@article{wu2025editreward,
  title={EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing},
  author={Wu, Keming and Jiang, Sicong and Ku, Max and Nie, Ping and Liu, Minghao and Chen, Wenhu},
  journal={arXiv preprint arXiv:2509.26346},
  year={2025}
}