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.
Власти Санкт-Петербурга выплатят деньги Гуменнику за шестое место на Олимпиаде-202620:57，详情可参考Line官方版本下载
Фото: Aziz Karimov / Reuters，这一点在快连官网中也有详细论述
Студенты нашли останки викингов в яме для наказаний14:52
Угрозу применения ядерного оружия в конфликте вокруг Ирана оценили14:57