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This may be an implementation detail to obfuscate GPT weights. OR it was to encourage selecting the best answers to further train the model.

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Pseudo random numbers are injected into the models via its temperature settings, but OpenAI could seed that to get the same answers with the same input. I’m going out on a limb here with pure speculation but given the model, a temperature, and a known text prompt, OpenAI could probably reverse engineer a seed and prove that the weights are the same.

fine-tuning original weights solves that, and any sane person would fine-tune for their task anyways to get better results

Since fine-tuning is often done by freezing all but the top layers I wonder if it would still be possible to take a set of inputs and outputs and mathematically demonstrate that a model is derivative of ChatGPT. There may well be too much entropy to unpack, but I’m sure there will be researchers exploring this, if only to identify AI-generated material.

Of course, since the model is so large and general purpose already, I can’t assume the same fine-tuning techniques are used as for vastly smaller models, so maybe layers aren’t frozen at all.

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