--- license: mit configs: - config_name: default data_files: - split: spatiotemporal_planning path: data/spatiotemporal_planning-* - split: embodied_grounding path: data/embodied_grounding-* - config_name: spatial_reasoning data_files: - split: spatial_reasoning path: data/spatial_reasoning-* dataset_info: - config_name: default features: - name: question_type dtype: string - name: image dtype: image - name: question dtype: string - name: answer dtype: string splits: - name: spatiotemporal_planning num_bytes: 666694379.4 num_examples: 49800 - name: embodied_grounding num_bytes: 1870487844 num_examples: 100000 download_size: 2498651415 dataset_size: 2537182223.4 - config_name: spatial_reasoning features: - name: video dtype: string - name: question dtype: string - name: answer dtype: string - name: question_type dtype: string splits: - name: spatial_reasoning num_bytes: 36647782 num_examples: 101000 download_size: 6232491 dataset_size: 36647782 language: - en size_categories: - 1M

GEM: Generative Supervision Helps Embodied Intelligence

Ruowen Zhao¹, Bangguo Li¹, Zuyan Liu^1,2,†, Yinan Liang¹, Junliang Ye¹, Fangfu Liu¹,
Diankun Wu¹, Zhengyi Wang¹, Xumin Yu², Yongming Rao^2,✉, Han Hu², Jun Zhu^1,✉
^†Project Lead.^✉Corresponding Author.
¹Tsinghua University, ²Tencent Hunyuan

## Abstract Embodied Vision-Language Models (VLMs) have demonstrated impressive performance and generalization in robotics, particularly within Vision-Language-Action frameworks. However, a significant gap remains between the high-level semantic focus of standard text-guided pre-training paradigms and the low-level spatial and physical knowledge critical for execution in embodied environments. In this paper, we introduce **GEM**, a Generative-supervised Embodied vision-language Model designed to bridge this divide. We propose integrating a depth map generation task directly into the VLM pre-training phase. By training this generative objective jointly with the main model, we observe substantial improvements in embodied intelligence, significantly enhancing both semantic understanding and physical operation capabilities. To support this paradigm, we curate and release GEM-4M, a comprehensive large-scale dataset featuring a mixture of grounding, reasoning, and planning data paired with high-quality depth supervision. Extensive experiments demonstrate that GEM achieves state-of-the-art results across diverse embodied benchmarks. Furthermore, our deployed action model, GEM-VLA, exhibits vastly superior task execution abilities in both simulation environments and real-world evaluations.

## Dataset Structure ``` GEM-250K/ ├── assets/ │ ├── GEM-demo.mp4 │ ├── overview.png ├── data/ │ ├── embodied_grounding-00000-of-00002.parquet │ ├── embodied_grounding-00001-of-00002.parquet │ ├── spatial_reasoning-00000-of-00001.parquet │ └── spatiotemporal_planning-00000-of-00001.parquet └── README.md ``` ## Citation If you find this dataset helpful, please cite: ``` @article{zhao2026gem, title={GEM: Generative Supervision Helps Embodied Intelligence}, author={Zhao, Ruowen and Li, Bangguo and Liu, Zuyan and Liang, Yinan and Ye, Junliang and Liu, Fangfu and Wu, Diankun and Wang, Zhengyi and Yu, Xumin and Rao, Yongming and others}, journal={arXiv preprint arXiv:2605.28548}, year={2026} } ```