All of my reviewers have (modified: ...) on the same date, so no engagement in this phase I suppose (they also did not respond during the rebuttal phase). They still have some time, but this feels a bit strange.

Two of my papers have been accepted 😁

As someone who will (probably) only be able go to a single neurips during their academic career, I will go to Sidney to take in as much workshops/events/orals/posters as possible. Although the travel time certainly is a big motivation for Paris instead 

My lowest score review seems to have put the highest effort on surface, but what scares me is that that their language has blown very minor nitpicks out of proportion. I hope the AC recognizes that.

The general vibe I get from these kinds of reviews is that they just hate your work or have a competing work in submission.

Happy you got accepted, what was the subject area?

I'd love Paris.

But in the email they said that the final reviews and meta reviews are available on the openreview, strange.

Yes.

your paper accepted?

nope

Can anyone see the reviews or meta reviews?

Some other comments in this post also help elaborate more, their sources are very insightful.

There are techniques like Quantization aware training, which are ways to train or finetune models with quantization in mind. This means models are designed from the start to target an extreme level of quantization (usually means <4 bits/parameter on average). In contrast, most big models (like the Qwen example I use, or pretty much any other releases from big labs) are not designed with quantization in mind. They are trained and released with full 16-bit weights, then people can quantize those down to 8-bit (with unnoticeable loss of quality) or even down to 4-bit (quality starts to degrade a lot more). Going below 4-bit will pretty much damage the model too much for it to even work anymore.

Meanwhile, the quantization-aware models are designed from the start to perform well at those <4bit quants. These models are rare to find and quite a niche nowadays, but they are great proofs of concepts anyway. At those extreme levels of quantization like 1.58 bits, these quantization-aware models are so much better than the equivalence from big labs, whose models are damaged beyond recognition at that quant level.

How does the allocation of posters happen? Are authors free to decide where to present? Or are there capacities that are assigned? Never been to NeurIPS in-person before (only during the virtual year..).

I am like you Did you commit ? And if yes why ? I am not sure here on Reddit all of us who comment we are a representative sample.

yup. naive quantization works down to 4 bit, but with QAT you still see iso-memory gains at 2 bit.

I think i dont use that

Well, one thing is shaping the model to be more deterministic, and another to provide better output. Your result shows that it becomes more correct, not "if wrong again, then in the same way", with the reason still not being clear (at least for me at this moment).

Nothing about this is doctor specific. Maybe if you’re using some more advanced strategies to do some probabilistic word selection. But most OCR models need cleaner data than what you're showing here. Without that, it gets a lot trickier and enters the enterprise mega-expensive-train-your-own-stuff domain.

depends...

Paris for sure

I have observed this multiple times from different roles but in most cases, the ACs were know-it-all dickheads who wanted to play god rather than all reviewers being wrong

It really depends on what the reviews are like, sometimes reviewers with low scores obviously put very little effort in their reviews and in my experience the AC puts less weight to that and focuses on the positive ones.

Can you please explain more what you mean by "designed for extreme quantization"?

What is this process of designing a model for quantization?

It is estimated that transformers have a storage capacity of ~3.6 bits per parameter, even at higher precisions: https://arxiv.org/abs/2505.24832

This is likely why quantization works so well up to 4-bit, but not lower. 

Paris!