---
title: MolmoAct-Pretraining-Mixture
canonical_url: "https://www.modelscope.cn/datasets/allenai/MolmoAct-Pretraining-Mixture"
md_url: "https://www.modelscope.cn/datasets/allenai/MolmoAct-Pretraining-Mixture.md"
repository: allenai/MolmoAct-Pretraining-Mixture
last_updated: 2025-09-10
license: cc-by-4.0
storage_size: "1.1 TB"
downloads: 4552
stars: 0
---

# MolmoAct-Pretraining-Mixture

> MolmoAct-Pretraining-Mixture - allenai 在 ModelScope 开源的数据集。MolmoAct - Pretraining Mixture

allenai/MolmoAct-Pretraining-Mixture 是 ModelScope 魔搭社区上的数据集，存储大小 1.1 TB，采用 cc-by-4.0 许可。

- **Repository**: allenai/MolmoAct-Pretraining-Mixture
- **License**: cc-by-4.0
- **Storage size**: 1.1 TB
- **Downloads**: 4552
- **Stars**: 0
- **Last updated**: 2025-09-10

Source: https://www.modelscope.cn/datasets/allenai/MolmoAct-Pretraining-Mixture

---

# MolmoAct - Pretraining Mixture


Data Mixture used for MolmoAct Pretraining. Contains a subset of OXE formulated as Action Reasoning Data along with auxiliary robot data and link to Multimodal Web data.



MolmoAct is a fully open-source action reasoning model for robotic manipulation developed by the Allen Institute for AI. MolmoAct is trained on a subset of OXE and MolmoAct Dataset, a dataset with 10k high-quality trajectories of a single-arm Franka robot performing 93 unique manipulation tasks in both home and tabletop environments. It has state-of-the-art performance among vision-language-action models on multiple benchmarks while being fully open-source. You can find all models in the MolmoAct family [here](https://huggingface.co/collections/allenai/molmoact-689697591a3936fba38174d7).
**Learn more about MolmoAct** in our announcement [blog post](https://allenai.org/blog/molmoact) or the [paper](https://huggingface.co/allenai/MolmoAct-7B-D-0812/blob/main/MolmoAct_Technical_Report.pdf).



## Dataset Description

**MolmoAct - Pretraining Mixture** contains third party content from Open X-Embodiment. (The data from other sources, Pixmo and Molmo Academic Dataset, will be referenced in the dataset card by linking only - i.e. we are not actually including the data from these sources in the pretraining mixture, so the only data in this dataset is from Open X-Embodiment). We convert the raw robot data using Depth-Anything v2 and Molmo 7B to Action Reasoning Data.


LVIS Bounding Box Dataset can be downloaded from [here](https://huggingface.co/datasets/wentao-yuan/robopoint-data).

Pixmo and Molmo Academic dataset can be downlaoded from [here](https://github.com/allenai/molmo).


## Dataset Statistics

- bc_z: 10289224 samples
- fractal20220817_data: 7065568 samples
- bridge_dataset: 3746792 samples
- auxiliary_depth: 1500000 samples
- auxiliary_line: 1500000 samples



Quick links:
- 📂 [All Models](https://huggingface.co/collections/allenai/molmoact-689697591a3936fba38174d7)
- 📂 [All Data](https://huggingface.co/collections/allenai/molmoact-data-mixture-6897e583e13b6c2cf3ea2b80)
- 📃 [Paper](https://arxiv.org/abs/2508.07917)
- 🎥 [Blog Post](https://allenai.org/blog/molmoact)
- 🎥 [Video](https://youtu.be/-_wag1X25OE?si=Xi_kUaJTmcQBx1f6)
- [Code](https://github.com/allenai/MolmoAct/tree/main)


## License and Use

This dataset is licensed under CC BY-4.0. It is intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use). The data is based on the RT-1 Robot Action, Berkeley Bridge, and BC-Z datasets from [Open X-Embodiment](https://github.com/google-deepmind/open_x_embodiment?tab=readme-ov-file). All other datasets linked in the documentation are subject to the respective licenses governing their use.   


## Citation

```
@misc{molmoact2025,
      title={MolmoAct: Action Reasoning Models that can Reason in Space}, 
      author={Jason Lee and Jiafei Duan and Haoquan Fang and Yuquan Deng and Shuo Liu and Boyang Li and Bohan Fang and Jieyu Zhang and Yi Ru Wang and Sangho Lee and Winson Han and Wilbert Pumacay and Angelica Wu and Rose Hendrix and Karen Farley and Eli VanderBilt and Ali Farhadi and Dieter Fox and Ranjay Krishna},
      year={2025},
      eprint={2508.07917},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2508.07917}
}
```