Initial release: array/SAT subset for Molmo2-ER training

README.md

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+---
+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`.

README_upstream.md

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+---
+license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "SAT_train.parquet"
+  - split: static
+    path: "SAT_static.parquet"
+  - split: val
+    path: "SAT_val.parquet"
+  - split: test
+    path: "SAT_test.parquet"
+dataset_info:
+  features:
+  - name: image_bytes
+    list:
+      dtype: image
+  - name: question
+    dtype: string
+  - name: answers
+    list:
+      dtype: string
+  - name: question_type
+    dtype: string
+  - name: correct_answer
+    dtype: string
+task_categories:
+- question-answering
+size_categories:
+- 100K<n<1M
+---
+# SAT: Spatial Aptitude Training for Multimodal Language Models
+
+[Project Page](https://arijitray1993.github.io/SAT/)
+
+![SAT Data](https://arijitray1993.github.io/SAT/SAT_webpage/static/images/sat_teaser.png)
+
+
+To use the dataset, first make sure you have Python3.10 and Huggingface datasets version 3.0.2 (`pip install datasets==3.0.2`):
+
+```python
+from datasets import load_dataset
+import io
+
+split = "val"
+dataset = load_dataset("array/SAT", batch_size=128)
+
+example = dataset[split][10] # example 10th item
+
+images = [Image.open(io.BytesIO(im_bytes)) for im_bytes in example['image_bytes']] # this is a list of images. Some questions are on one image, and some on 2 images
+
+question = example['question']
+answer_choices = example['answers'] # please randomize this if you present as choices to MLM, or perform circular eval.
+correct_answer = example['correct_answer']
+
+```
+
+The available `split` choices are:
+- `train`: (175K image QA pairs) Train split of SAT data that includes both static relationships and dyamic spatial QAs involving object and scene motion. For motion-based questions, there are two images. 
+- `static`:  (127K image QA pairs) Train split of SAT data that includes _only_ static QAs. Always has one image only.
+- `val`: (4K image QA pairs) Synthetic validation split. 
+- `test`: (150 image QA pairs) Real-image dynamic test set.  **We highly recommend performing circular eval (test with all positions of the correct answer among answer choices) since this split is small. The paper results are with circular eval.**
+
+
+
+
+If you find this data useful, please consider citing:
+
+```
+@misc{ray2025satdynamicspatialaptitude,
+      title={SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models}, 
+      author={Arijit Ray and Jiafei Duan and Ellis Brown and Reuben Tan and Dina Bashkirova and Rose Hendrix and Kiana Ehsani and Aniruddha Kembhavi and Bryan A. Plummer and Ranjay Krishna and Kuo-Hao Zeng and Kate Saenko},
+      year={2025},
+      eprint={2412.07755},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2412.07755}, 
+}
+```

SAT_train.parquet

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+oid sha256:7103623171c5270a633d7ba9bc646b61bb4401e3efe3b9d1dc441762e082821a
+size 4558508645