Seed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911498982391808 |
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| author | Tai, Cong Zheng, Zhaoyu Long, Haixu Wu, Hansheng Long, Zhengbin Xiang, Haodong Shi, Rong Cui, Zhuo Zhang, Shizhuang Qiu, Gang Wang, He Li, Ruifeng Liu, Biao Sun, Zhenzhe Shen, Tao |
| author_facet | Tai, Cong Zheng, Zhaoyu Long, Haixu Wu, Hansheng Long, Zhengbin Xiang, Haodong Shi, Rong Cui, Zhuo Zhang, Shizhuang Qiu, Gang Wang, He Li, Ruifeng Liu, Biao Sun, Zhenzhe Shen, Tao |
| contents | Existing data generation methods suffer from exploration limits, embodiment gaps, and low signal-to-noise ratios, leading to performance degradation during self-iteration. To address these challenges, we propose Seed2Scale, a self-evolving data engine that overcomes the data bottleneck through a heterogeneous synergy of "small-model collection, large-model evaluation, and target-model learning". Starting with as few as four seed demonstrations, the engine employs the lightweight Vision-Language-Action model, SuperTiny, as a dedicated collector, leveraging its strong inductive bias for robust exploration in parallel environments. Concurrently, a pre-trained Vision-Language Model is integrated as a Verifer to autonomously perform success/failure judgment and quality scoring for the massive generated trajectories. Seed2Scale effectively mitigates model collapse, ensuring the stability of the self-evolution process. Experimental results demonstrate that Seed2Scale exhibits signifcant scaling potential: as iterations progress, the success rate of the target model shows a robust upward trend, achieving a performance improvement of 131.2%. Furthermore, Seed2Scale signifcantly outperforms existing data augmentation methods, providing a scalable and cost-effective pathway for the large-scale development of Generalist Embodied AI. Project page: https://terminators2025.github.io/Seed2Scale.github.io |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_08260 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Seed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation Tai, Cong Zheng, Zhaoyu Long, Haixu Wu, Hansheng Long, Zhengbin Xiang, Haodong Shi, Rong Cui, Zhuo Zhang, Shizhuang Qiu, Gang Wang, He Li, Ruifeng Liu, Biao Sun, Zhenzhe Shen, Tao Robotics Existing data generation methods suffer from exploration limits, embodiment gaps, and low signal-to-noise ratios, leading to performance degradation during self-iteration. To address these challenges, we propose Seed2Scale, a self-evolving data engine that overcomes the data bottleneck through a heterogeneous synergy of "small-model collection, large-model evaluation, and target-model learning". Starting with as few as four seed demonstrations, the engine employs the lightweight Vision-Language-Action model, SuperTiny, as a dedicated collector, leveraging its strong inductive bias for robust exploration in parallel environments. Concurrently, a pre-trained Vision-Language Model is integrated as a Verifer to autonomously perform success/failure judgment and quality scoring for the massive generated trajectories. Seed2Scale effectively mitigates model collapse, ensuring the stability of the self-evolution process. Experimental results demonstrate that Seed2Scale exhibits signifcant scaling potential: as iterations progress, the success rate of the target model shows a robust upward trend, achieving a performance improvement of 131.2%. Furthermore, Seed2Scale signifcantly outperforms existing data augmentation methods, providing a scalable and cost-effective pathway for the large-scale development of Generalist Embodied AI. Project page: https://terminators2025.github.io/Seed2Scale.github.io |
| title | Seed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.08260 |