---
license: mit
language:
- en
pretty_name: Molmo2-ER SAT
tags:
- embodied-reasoning
- molmo2
- molmo2-er
- vlm-training-data
---

# Molmo2-ER · array/SAT

Spatial Aptitude Training: 175K binary-MCQ VQA pairs over ProcTHOR indoor scenes.

This is a re-hosted, **loader-ready subset** of the upstream dataset, used to train [`allenai/Molmo2-ER-4B`](https://huggingface.co/allenai/Molmo2-ER-4B). Files mirror the upstream layout; nothing in the data has been modified.

## Upstream source

- **Original dataset:** [array/SAT](https://huggingface.co/datasets/array/SAT)
- **Paper:** *SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models* ([arXiv:2412.07755](https://arxiv.org/abs/2412.07755))
- **License:** `mit` (inherits from upstream)

If you use this data, please cite the original authors:

```bibtex
@misc{ray2025satdynamicspatialaptitude,
  title={SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models},
  author={Arijit Ray and Jiafei Duan and Ellis Brown and others},
  year={2025},
  eprint={2412.07755},
  archivePrefix={arXiv}
}
```

## Usage in Molmo2-ER

See the [`allenai/molmo2`](https://github.com/allenai/molmo2) repository for the data loader and training recipe. The relevant loader class for this dataset lives in `olmo/data/spatial_datasets.py`.