Why do OpenAI's GPT-2 weights beat mine? :: Giles' blog
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Why do OpenAI's GPT-2 weights beat mine?
Posted on 29 July 2026
in
GPT-2 mysteries,
AI
When I finished my project training an LLM from scratch, I was<br>left with a minor mystery. Why were my models worse at instruction-following than the original OpenAI<br>GPT-2 small weights?
I had an evaluation that I was running,<br>based on the instruction fine-tuning code in chapter 7 of<br>"Build a Large Language Model (from Scratch)".<br>The process was to train a model on samples from the Alpaca<br>instruction-following dataset until validation loss started rising, to use that instruction fine-tuned model to generate<br>completions to a held-back test set, and then to use an LLM to compare the<br>results from various different models. The details are here;<br>let's call it the IFT eval.
OpenAI's original weights for GPT-2 small consistently beat my own<br>models, even when mine got better results than theirs on a more technical<br>evaluation, where I just measured the cross entropy loss for each model on a<br>held-back set of test sequences. This surprised me; I would have expected<br>a reasonably close correlation between the two evals -- that better test loss would imply<br>better instruction-following.
I have a couple of thoughts about why this might be, and given that I recently<br>set up poppy, my dedicated LLM training box<br>I decided to inaugurate her with an experiment to test one of them; further<br>experiments will come in time -- though I don't think this will be a focus for the blog. More<br>of a running theme, with occasional posts until either I solve the mystery, or<br>give up in despair...
In this post I'll give a bit more detail about the nature of the problem, and<br>list some of the things I've been thinking might be the cause. In the next post, I'll give the results<br>of the first experiment.
More on the mystery
Let's take a look at the results...