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Isaac 0.5 is a 36-billion-parameter AI model that efficiently adapts to various robotic embodiments by leveraging 1M hours of video and 100K hours of robotic interaction data, demonstrating a scaling law where increased video data significantly reduces teleoperation requirements.
This blog from NVIDIA Research discusses how sequence parallelism can scale long-video training systems for both understanding and generation, addressing the challenge of fitting very long video sequences across multiple GPUs.