A quick(ish) Chinchilla check

I recently overtrained a couple of GPT-2 style models, training them both on 40 tokens per parameter rather than the 20 per parameter that is generally regarded as "Chinchilla-optimal". The normal heuristic is that instead of doing that, you should scale up the number of tokens and the number of parameters equally -- so I would have been better off scaling up the model by 2 and the token count by the same amount. By doing that, I should expect to get a better model in terms of loss on my held-back test set than I did with my 40-tokens-per-parameter models. My training machine poppy wasn't doing anything, so I decided to give that a go. Would the Chinchilla rule-of-thumb hold up? As you might expect, it did. But it was a surprisingly close-run thing, and could conceivably have been in the noise. Let's take a look. The Chinchilla heuristic If you already know all about the Chinchilla paper -- regular readers in particular must be sick and tired of it by now :-) -- then click here to…