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pretty_name: AVM-Pref
size_categories:
- 100K<n<1M
tags:
- robotics
- world-models
- preference-data
- reward-modeling
- arxiv:2610.32222
---
# AVM-Pref
**AVM-Pref (Action and Video Mismatch Preference Dataset)** provides complementary preference supervision for training reward models that jointly assess **action-following fidelity** and **visual quality** in action-conditioned world models. It is introduced in [ACWM-RM: A Reference-Free Reward Model from Complementary Mismatch Preferences](https://arxiv.org/abs/2610.32222).
## Dataset Overview
The dataset described in the paper contains approximately **506.3k preference pairs**, constructed from the AgiBotWorld training subset:
| Pair type | Size | Training supervision |
| --- | --- | --- |
| Action mismatch | 156.3k | Original versus numerically or semantically perturbed action sequences for the same real video, supervising action-following fidelity. |
| Video mismatch | 350k | Generated videos under the same initial frame and action sequence, ranked using global and local PSNR and LPIPS to supervise visual quality. Includes intra-model and cross-model pairs. |
Generated videos come from **DreamDojo-2B, DreamDojo-14B, GE-Sim-2B, and OSCAR-2B**. ACWM-RM learns from these preferences using the Bradley-Terry loss. For construction and training details, please refer to the paper.
## Files
```text
train/
action_mismatch/ # Training action-mismatch pairs (Parquet)
video_mismatch/ # Training video-mismatch pairs (Parquet)
val/
action_mismatch/ # Validation action-mismatch pairs (Parquet)
video_mismatch/ # Validation video-mismatch pairs (Parquet)
_media/
agibotworld/observations/ # Real-video archives by task
wm/ # Generated-video archives by world model and split
```
For evaluation, see [AVM-Eval](https://huggingface.co/datasets/SPUH/AVM-Eval).
## Citation
If you use AVM-Pref in your research, please cite:
```bibtex
@misc{hong2026acwmrm,
title = {{ACWM-RM}: A Reference-Free Reward Model from Complementary Mismatch Preferences},
author = {Shibo Hong and Siyu Ren and FengJiao Chen and Zhongyuan Peng and Wei Shi and Yixin Cao},
year = {2026},
eprint = {2610.32222},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.32222}
}
```
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