VEFX-Reward-32B
VEFX-Reward-32B is the 32B-parameter variant of the VEFX-Reward family — a video editing quality reward model based on Qwen3-VL-32B-Instruct. It scores text-driven video edits on three dimensions on a 1–4 scale:
| Dimension | What it measures |
|---|---|
| IF — Instructional Following | Does the edit accurately reflect the editing instruction? |
| RQ — Render Quality | Visual clarity, temporal consistency, physical plausibility |
| EE — Edit Exclusivity | Were only the intended regions modified, without side-effects? |
- 📄 Paper: VEFX-Bench
- 💻 Code: Visko-Platform/VEFX-Bench
- 🤗 Dataset: xiangbog/VEFX-Bench
- 🤗 Smaller model: xiangbog/VEFX-Reward-4B
- 🏆 Leaderboard: vefx-leaderboard.com
Quick Start
git clone https://github.com/Visko-Platform/VEFX-Bench.git
cd VEFX-Bench
pip install -e .
from vefx_reward import VEFXReward
model = VEFXReward("viskoplatform/VEFX-Reward-32B", device="cuda")
scores = model.score(
original_video="examples/sample_videos/object_removal_original.mp4",
edited_video="examples/sample_videos/object_removal_edited.mp4",
instruction="Remove the woman with the grey backpack walking on the right side of the frame.",
)
print(scores)
# {'IF': 3.69, 'RQ': 3.70, 'EE': 3.26, 'Overall': 10.65}
Hardware: ~65 GB VRAM (bfloat16). Tested on a single NVIDIA H100 80 GB.
Citation
@article{gao2026vefx,
title={VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects},
author={Gao, Xiangbo and Jiang, Sicong and Liu, Bangya and Chen, Xinghao and Yang, Minglai and Yang, Siyuan and Wu, Mingyang and Yu, Jiongze and Zheng, Qi and Wang, Haozhi and others},
journal={arXiv preprint arXiv:2604.16272},
year={2026}
}
License
Apache 2.0.