Seed2Scale: A Self-Evolving Data Engine with Parallel Worlds
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Seed2Scale: A Self-Evolving Data Engine with Parallel Worlds Expansion for Scalable Robot Learning. This paper presents Seed2Scale, a self-evolving data engine designed to overcome challenges in robot learning, such as limited exploration and domain shifts. Starting with minimal seed demonstrations, Seed2Scale uses a c…
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Reported quantity · Reported success rate: 75.38 percent · Basis not reported · Reported zero-shot result; the excerpt does not specify an evaluation protocol.
“at Seed2Scale exhibits significant scaling potential: as iterations progress, the success rate of the target model shows a consistent upward trend, significantly outperforming the seed baseline. Notably, Seed2Scale achieves a remarkable 75.38% success rate in zero-shot real-world evaluations, where baseline methods fail completely (0%). Project page: https://terminators2025.github.io/Seed2Scale.github.io”
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- arXiv Robotics — research abstractsSeed2Scale: A Self-Evolving Data Engine with Parallel Worlds Expansion for Scalable Robot Learning
This paper presents Seed2Scale, a self-evolving data engine designed to overcome challenges in robot learning, such as limited exploration and domain shifts. Starting with minimal seed demonstrations, Seed2Scale uses a combination of lightweight models for data collection and verification, followed by parallel worlds expansion to generate diverse data. Experimental results show strong scaling potential, with a 75.38% success rate in zero-shot real-world evaluations.
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