| 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`. | |