Comment by icyfox | Hacker News Reader

Comment by icyfox | Hacker News Reader

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I guarantee you the weights are already versioned like you're describing. Each training run results in a static bundle of outputs and these are very much pinned (OpenAI has confirmed multiple times that they don't change the model weights once they issue a public release).

> Not quantized. Weights are the same. If we did change the model, we’d release it as a new model with a new name in the API.”

- [Ted Sanders](https://news.ycombinator.com/item?id=44242198) (OpenAI)

The problem here is that most issues stem from broader infrastructure issues like numerical instability at inference time. Since this affects their whole service pipeline, the logic here can't really be encapsulated in a frozen environment like a Docker container. I suppose _technically_ they could maintain a separate inference cluster for each of their point releases, but that also means that previous models don't benefit from common infrastructure improvements / load balancing would be more difficult to shard across GPUs / might be logistically so hard to coordinate to effectively make it impossible.

https://www.anthropic.com/engineering/a-postmortem-of-three-... https://thinkingmachines.ai/blog/defeating-nondeterminism-in...

Sorry, but this makes no sense. Numerical instability would lead to random fluctuations in output quality, but not to a continuous slow decline like the OP described.

Heard of similar experiences from RL acquaintances, where a prompt worked reliably for hundreds of requests per day for several months - and then suddenly the model started to make mistakes, ignore parts of the prompt, etc when a newer model was released.

I agree, it doesn't have to be deliberate malice like intentionally nerfing a model to make people switch to the newer one - it might just be that less resources are allocated to the older model once the newer one is available and so the inference parameters change - but some effect at the release of a newer model seems to be there.

I'm responding to the parent comment who's suggesting we version control the "model" in Docker. There are infra reasons why companies don't do that. Numerical instability is one class of inference issues, but there can be other bugs in the stack separate from them intentionally changing the weights or switching to a quantized model.

As for the original forum post:

- Multiple numerical computation bugs can compound to make things worse (we saw this in the latest Anthropic post-mortum)

- OP didn't provide any details on eval methodology, so I don't think it's worth speculating on this anecdotal report until we see more data

Good points. And I also agree we'd have to see the data that OP collected.

If it indeed did show a slow decline over time and OpenAI did not change the weights, then something does not add up.

That's a great point. However, I think we can treat the serving pipeline as part and parcel of the model, for practical purposes. So it is dishonest of companies to say they haven't changed the model while undertaking such cost optimizations that impair the models' effective intelligence.

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