{"ID":2839222,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16651","arxiv_id":"2511.16651","title":"InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy","abstract":"Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of large-scale real-robot pre-training, synthetic data has not previously demonstrated comparable capability at scale. This paper provides the first evidence that synthetic data alone can match the performance of the strongest $π$-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation. The resulting model also exhibits surprisingly zero-shot sim-to-real transfer on several challenging tasks. Our synthetic dataset, InternData-A1, contains over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation. It is generated through a highly autonomous, fully decoupled, and compositional simulation pipeline that enables long-horizon skill composition, flexible task assembly, and heterogeneous embodiments with minimal manual tuning. Using the same architecture as $π_0$, we pre-train a model entirely on InternData-A1 and find that it matches the official $π_0$ across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks. We release the dataset and will open-source the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research.","short_abstract":"Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of large-scale real-robot pre-training, synthetic data has not previously demonstrated comparable capability at scale. This paper provides the...","url_abs":"https://arxiv.org/abs/2511.16651","url_pdf":"https://arxiv.org/pdf/2511.16651v1","authors":"[\"Yang Tian\",\"Yuyin Yang\",\"Yiman Xie\",\"Zetao Cai\",\"Xu Shi\",\"Ning Gao\",\"Hangxu Liu\",\"Xuekun Jiang\",\"Zherui Qiu\",\"Feng Yuan\",\"Yaping Li\",\"Ping Wang\",\"Junhao Cai\",\"Jia Zeng\",\"Hao Dong\",\"Jiangmiao Pang\"]","published":"2025-11-20T18:55:05Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}