The LLM Lobotomy?

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...

In addition to quantization, I suspect the additions they make continually to their hidden system prompt for legal, business, and other reasons slowly degrade responses over time as well.

They test specific prompts with temperature 0. It is of course possible that all their tests prompts were lucky, but still then, shouldn't you see an immediate drop followed by a flat or increasing line?

Also, from what I understand from the article, it's not a difficult task but an easily machine checkable one, i.e. whether the output conforms to a specific format.

Yes exactly, my theory is that the novelty of a new generation of LLMs’ performances tends to cause an inflation in peoples’ perceptions of the model, with a reversion to a better calibrated expectation over time. If the developer reported numerical evaluations that drifted over time, I’d be more convinced of model change.

TFA is about someone running the same test suite with 0 temperature and fixed inputs and fixtures on the same model over months on end.

What’s missing is the actual evidence. Which I would love of course. But assuming they’re not actively lying, this is not as subjective as you suggest.

your theory does not hold up for this specific article as they carefully explained they are sending identical inputs into the model each time and observing progressively worse results with other variables unchanged. (though to be fair, others have noted they provided no replication details as to how they arrived at these results.)

fta: “I am glad I have proof of this with the test system”

I think they have receipts, but did not post them there

Did any of you read the article? They have a test framework that objectively shows the model getting worse over time.

I don't remember where I saw it, but I remember a claim that Azure hosted models performed poorer than those hosted by openAI.

Even with temperature 0, the LLM output will not be deterministic. It will just have less randomness (not defined precisely) than with temperature 1. There was a recent post on the frontpage about fully deterministic sampling, but it turns out to be quite difficult.

It could be that performance on temp zero has declined but performance on a normal temp is the same or better.

I wonder if temp zero would be more influenced by changes to the system prompt too. I can imagine it making responses more brittle.

I'd have assumed a fixed seed was used, but he doesn't mention that. Weird. Maybe he meant that?