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• Seed2Scale achieves a 131.2% performance improvement in the target model's success rate through iterative self-evolution.result 90%
• Seed2Scale can start with as few as four seed demonstrations to initiate the self-evolving data engine.method 90%
• Seed2Scale employs a lightweight Vision-Language-Action model called SuperTiny as a dedicated collector for robust exploration.method 90%
• Seed2Scale uses a pre-trained Vision-Language Model as a Verifier to autonomously perform success/failure judgment and quality scoring.method 90%
• Seed2Scale effectively mitigates model collapse, ensuring stability of the self-evolution process.technical 85%
• Seed2Scale significantly outperforms existing data augmentation methods for embodied AI development.result 85%
• Seed2Scale provides a scalable and cost-effective pathway for large-scale development of Generalist Embodied AI.market 80%
• Existing data generation methods suffer from exploration limits, embodiment gaps, and low signal-to-noise ratios.limitation 90%

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