That's a fair point and I'm not sure why I didn't consider that they would release a pre-print after they had submitted it. (This is a total fumble on my part)

> there is an issue here about big teams breaking the blind review process by putting themselves on the front

I don't see that as actually breaking the blind review part. There are many more abuses that de-anonymize themselves. Most transformer research is done by big labs because they need the processing power and are the only ones who can afford such equipment (though there was a paper that did transformers on CPUs). Just training ImageNet is out of bounds for a lot of people (I have a few A6000s and it still takes me days). A trivial example is that Google will use JFT and will include it everywhere. If you're qualified to review you're probably going to be able to de-anonymize the lab. I do think we need to do more to make a more level playing field but that's an extremely difficult thing to do. More resources just enables you do do more. But maybe we shouldn't metric hack as much, which would slow things down a little.

The scientific method is being followed here. Code is not needed for the scientific model to be followed. Even data. Literally every other field is able to advance without public code or data (in fact most areas of CS). There's absolutely no reason to believe that they won't release their code. They have a history of doing so. Models and checkpoints are not the bare-minimum for reproducibility. They describe their model enough in the paper. There's enough written in the paper (which is 30 pages) to reproduce the model. Will it be easy? No. But it can be done. And to be clear, I'm saying that the status quo of code being released is a godsend. This is not the norm in literally every other field/subfield. Code helps with reproducibility (and so should be encouraged) but is not required.

If you require someone else's code to reproduce results then you're not convincing me you're a good ML researcher nor programmer.

That said, I agree with your overall position on ML publications. So much of what we see is a tech demo protected by some kind of moat, either a private commercial dataset or insatiable processing requirements or missing code or a combination of the above. These aren’t science, they’re advertisements.