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.
If that doesn’t yield results, the on-call engineer turns to our custom-built tool called “guild timings.” Every time a guild processes an action, it records how much of the current minute has been spent on each action type to an in-memory store. This data is much more detailed than our metrics, but it’s emitted at such a high volume that we can’t feasibly store it all. As such, this data is rotated frequently for all but our largest guilds. Even if we retrieve the data in time, it still won’t give us a good picture of the end-to-end experience, as it doesn’t capture downstream effects.，推荐阅读搜狗输入法获取更多信息
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