Why do OpenAI's GPT-2 weights beat mine? Part three: testing overtraining :: Giles' blog
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Why do OpenAI's GPT-2 weights beat mine? Part three: testing overtraining
Posted on 31 July 2026
in
GPT-2 mysteries,
AI,
JAX,
Python
The GPT-2-style models that I've been training work really well, and I've even<br>managed to train some that perform better than the original OpenAI small model<br>in terms of cross entropy loss on a test set. But<br>as I wrote previously,<br>there's a mystery: why do they perform worse on my instruction fine-tuning evaluation?
I had various theories about why that might be, and to me, the most plausible-seeming of them<br>was the amount of data they were trained with. As best I can find out, OpenAI's models were, by modern<br>standards, trained on much more data than they should have been, while I'd used<br>the theoretically optimal amount of training data.
To put it in other words, OpenAI's models were overtrained. If I deliberately<br>overtrained my own models, could I match their performance? This post is a write-up<br>of what happened, but so as not to bury the lede -- it didn't seem to help much, if at all.
Let's see why.
Overtraining
Let's start by getting a nice crisp definition of overtraining.
It's important not to confuse overtraining with overfitting. Overfitting is<br>where you train a model so that instead of learning a general rule about the<br>data it's seeing, it learns something very specific to the training data -- for example,<br>this:
...rather than this:
Overfitting is pretty much always a bad thing.
Overtraining, by contrast, is more of a judgement call. For LLMs, it's generally used as a shorthand for<br>"training for more than the Chinchilla-optimal number of tokens". The<br>Chinchilla paper makes a very specific case:<br>if you train a model for roughly 20 times as many tokens as it has parameters, then<br>you'll have as good a model as you can get for...