cvis-tmu/Spatial-SSRL-81k

Spatial-SSRL-81k

📖<a href="https://arxiv.org/abs/2510.27606">Paper</a>| 🏠<a href="https://github.com/InternLM/Spatial-SSRL">Github</a> |🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a> | 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-3B">Spatial-SSRL-3B Model</a> | 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-Qwen3VL-4B">Spatial-SSRL-Qwen3VL-4B Model</a> | 🤗<a href="https://huggingface.co/datasets/internlm/Spatial-SSRL-81k">Spatial-SSRL-81k Dataset</a> | 📰<a href="https://huggingface.co/papers/2510.27606">Daily Paper</a>

Spatial-SSRL-81k is a training dataset for enhancing spatial understanding in large vision-language models. It contains 81,053 samples of five pretext tasks for self-supervised learning, offering simple, intrinsic supervision that scales RLVR efficiently.

📢 News

• —🚀 [2026/04/05] We have released the training code of Spatial-SSRL.
• —🚀 [2026/02/25] We have released the 🤗Spatial-SSRL-3B Model, initialized from Qwen2.5-VL-3B-Instruct.
• —🚀 [2026/02/21] Our work has been accepted by CVPR 2026.
• —🚀 [2025/11/24] We have released the 🤗Spatial-SSRL-Qwen3VL-4B Model, initialized from Qwen3-VL-4B-Instruct.
• —🚀 [2025/11/03] Now you can try out Spatial-SSRL-7B on 🤗Spatial-SSRL Space.
• —🚀 [2025/11/03] We have released the 🤗Spatial-SSRL-7B Model, and 🤗Spatial-SSRL-81k Dataset.
• —🚀 [2025/11/02] We have released the 🏠Spatial-SSRL Repository.

🌈 Overview

We are thrilled to introduce <strong>Spatial-SSRL</strong>, a novel self-supervised RL paradigm aimed at enhancing LVLM spatial understanding. By optimizing Qwen2.5-VL-7B with Spatial-SSRL, the model exhibits stronger spatial intelligence across seven spatial understanding benchmarks in both image and video settings. </p> <p style="text-align: center;"> <img src="assets/teaser_1029final.png" alt="Teaser" width="100%"> </p> Spatial-SSRL is a <strong>lightweight</strong> tool-free framework that is natually compatible with the RLVR training paradigm and easy to extend to a multitude of pretext tasks. Five tasks are currently formulated in the framework, requiring only ordinary RGB and RGB-D images. <strong>And we welcome you to join Spatial-SSRL with effective pretext tasks to further strengthen the capabilities of LVLMs!</strong>

<p style="text-align: center;"> <img src="assets/pipeline_1029final.png" alt="Pipeline" width="100%"> </p>

💡 Highlights

• —🔥 Highly Scalable: Spatial-SSRL uses ordinary raw RGB and RGB-D images instead of richly-annotated public datasets or manual labels for data curation, making it highly scalable.
• —🔥 Cost-effective: Avoiding the need for human labels or API calls for general LVLMs throughout the entire pipeline endows Spatial-SSRL with cost-effectiveness.
• —🔥 Lightweight: Prior approaches for spatial understanding heavily rely on annotation of external tools, incurring inherent errors in training data and additional cost. In constrast, Spatial-SSRL is completely tool-free and can easily be extended to more self-supervised tasks.
• —🔥 Naturally Verifiable: Intrinsic supervisory signals determined by pretext objectives are naturally verifiable, aligning Spatial-SSRL well with the RLVR paradigm. <p style="text-align: center;"> <img src="assets/comparison_1029final.png" alt="Teaser" width="100%"> </p>

🖼️ Task examples

<p style="text-align: center;"> <img src="assets/task1.png" alt="Teaser" width="100%"> </p> <p style="text-align: center;"> <img src="assets/task2.png" alt="Teaser" width="100%"> </p> <p style="text-align: center;"> <img src="assets/task3.png" alt="Teaser" width="100%"> </p> <p style="text-align: center;"> <img src="assets/task4.png" alt="Teaser" width="100%">

🛠️ Usage

You can find all question-answering pairs in spatialssrl.parquet and the images in images.zip. The images are organized in five folders, each corresponding to a 2D or 3D pretext task. See the formulation of each task in 📖<a href="https://arxiv.org/abs/2510.27606">Paper</a> if you are interested.

✒️Citation

If you find this dataset useful, please kindly cite:

@article{liu2025spatial,
  title={Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning},
  author={Liu, Yuhong and Zhang, Beichen and Zang, Yuhang and Cao, Yuhang and Xing, Long and Dong, Xiaoyi and Duan, Haodong and Lin, Dahua and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2510.27606},
  year={2025}
}

📄 License

Usage and License Notices: The data and code are intended and licensed for research use only.