metadata
license: mit
base_model:
- CodeGoat24/UnifiedReward-2.0-qwen3vl-4b
Model Summary
UnifiedReward-Edit-qwen3vl-4b is a unified reward model for both Text-to-Image and Image-to-Image generation!! For image editing reward task, our models support:
Pairwise Rank — directly judge which of two edited images is better.
Pairwise Score — assign a separate score to each image in a pair.
Pointwise Score — rate a single image on two axes: instruction-following and overall image quality.
🚀 The image editing reward inference code is available at UnifiedReward-Edit/ directory, while T2I inference code is unchanged from previous models. The editing training data is preprocessed from EditScore, EditReward, and Pico-Nano-Banana. We sincerely appreciate all contributors!!
For further details, please refer to the following resources:
- 📰 Paper: https://arxiv.org/pdf/2503.05236
- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
- 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
- 👋 Point of Contact: Yibin Wang
Citation
@article{unifiedreward,
title={Unified reward model for multimodal understanding and generation},
author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi},
journal={arXiv preprint arXiv:2503.05236},
year={2025}
}