---
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}
}
```