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Eric Jang
@ericjang11
加入 January 2014
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Congratulations to the @DynaRobotics team. This is probably my favorite "scaling laws for robots" blog post so far. I hope that it *does not* remain my favorite, and that the community continues to raise the bar further. Next phase: robot brain companies start to expose inference endpoints or remote "ask me anything" sessions to test out their model on their robot. My favorite part of this blog post is that it provides enough detail that the result could be reproduced by an external lab (1M hours egocentric data is quite obtainable). They even evaluated on setups that any lab could buy (ABC-style bimanual YAM). Robotics is entering a scale-up era. The scale of investment is very serious, and so warrants serious rigor when companies make claims about models that only they can verify. Otherwise, we risk vaporizing billions of VC dollars underwritten by self-reported evaluations of capabilities.
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Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵
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