Last week we released NanoGPT Slowrun , an open repo for data-efficient learning algorithms. The rules are simple: train on 100M tokens from FineWeb, use as much compute as you want, lowest validation loss wins. Improvements are submitted as PRs to the repo and merged if they lower val loss. The constraint is the inverse of speedruns like modded-nanogpt , which optimize wall-clock time. Those benchmarks have been hugely productive, but optimizing for speed filters out expensive ideas: heavy regularization, second-order optimizers, gradient descent alternatives. Slowrun is built for exactly those ideas.
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Пьяный турист нанес тяжелую травму участвовавшей в Олимпиаде сноубордистке20:38
The Nature of Fun. But I don’t think either of the first two reasons would be enough to prevent a truly compelling new game experience from making itself known. I think what’s really going on here is that these new models just aren’t the natural source of fun I thought they would be when I first encountered them. Some forms of computation end up being a deep well of fun. 3D rendering is fun. Physics engines are fun. But these new AI models, despite being powerful and useful and fascinating, don’t seem to be intrinsically fun. Which I think is kind of surprising!