Model Summary
This model is GRPO trained using UnifiedReward-Flex as reward on the training dataset of UniGenBench.
🚀 The inference code is available at Github.
For further details, please refer to the following resources:
- 📰 Paper: https://arxiv.org/abs/2602.02380
- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/flex
- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-flex
- 🤗 Dataset: https://huggingface.co/datasets/CodeGoat24/UnifiedReward-Flex-SFT-90K
- 👋 Point of Contact: Yibin Wang
Qualitative Results
Quantitative Results
UniGenBench
| Model | Overall | Style | World Knowledge | Attribute | Action | Relationship | Compound | Grammar | Logical Reasoning | Entity Layout | Text Generation |
|---|---|---|---|---|---|---|---|---|---|---|---|
| FLUX2.Klein-base-9B | 78.93% | 97.50% | 91.61% | 83.65% | 77.00% | 86.42% | 78.61% | 76.87% | 53.41% | 88.43% | 55.75% |
| Ours | 81.54% | 97.60% | 91.93% | 85.47% | 78.42% | 86.42% | 81.96% | 76.97% | 58.64% | 88.43% | 69.54% |
T2I-CompBench
| Model | Overall | Color | Shape | Texture | 2D-Spatial | 3D-Spatial | Numeracy | Non-Spatial | Complex |
|---|---|---|---|---|---|---|---|---|---|
| FLUX2.Klein-base-9B | 53.72% | 85.90% | 60.81% | 72.24% | 41.46% | 36.87% | 64.36% | 31.11% | 37.04% |
| Ours | 58.75% | 85.93% | 63.36% | 74.69% | 46.77% | 43.18% | 70.60% | 30.73% | 54.73% |
GenEval
| Model | Overall | Single Object | Two Object | Counting | Colors | Position | Color Attr |
|---|---|---|---|---|---|---|---|
| FLUX2.Klein-base-9B | 78.99% | 99.69% | 92.93% | 77.50% | 92.55% | 66.75% | 44.50% |
| Ours | 81.55% | 99.69% | 93.94% | 84.69% | 93.22% | 70.75% | 47.00% |
Citation
@article{unifiedreward-flex,
title={Unified Personalized Reward Model for Vision Generation},
author={Wang, Yibin and Zang, Yuhang and Han, Feng and Bu, Jiazi and Zhou, Yujie and Jin, Cheng and Wang, Jiaqi},
journal={arXiv preprint arXiv:2602.02380},
year={2026}
}