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this is exactly what Slowrun aims to solve. i think the main problem is not the loss function but the learning algorithm:- gradient descent massively underutilizes compute relative to the data it's trained on. i think brains use a lot of compute to search for solutions that generalize, rather than doing computationally cheap but unexploratory curve fitting.- we've already gotten 10x better sample efficiency with Slowrun by pulling on two levers that let us utilize a lot of compute: heavy overpar
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If you’re arguing that “the Septuagint” or “the Dead Sea Scrolls,” both included certain books, and on that basis we must have those books in our Bibles today, then you have a big problem. Both “the Septuagint” and “the Dead Sea Scrolls” are mini-libraries — they include documents considered both scriptural and non-scriptural in their day. 

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