I think your leading statement is a bit more authoritative than what the linked paper actually demonstrates. It demonstrates your claimed qualities for specific algorithms, methodologies, and models.

It explicitly says that Quantization-Aware Training is out of scope, may reveal different trends and has potential for extreme quantization. Heavily-quantized model releases today generally use QAT or something similar, so I don't think the paper can be universally applied to modern quants (e.g. [0]).

That said, I don't see any evidence that TFA does any of that so the paper's claims may be quite applicable there.

[0]: https://huggingface.co/vcruz305/DSV4.1-Flash-1017-SAGE-REXL3...

I recently did some toying around with (very) small language models (<200k). I used QAT from the beginning and was surprised that I got byte identical results from the int8 model vs the float32 one.

If that scales with parameters I don't see why QAT shouldn't be the norm.

(As I was only comparing the QAT f32 model vs the quantization its possible that a not QAT f32 would have performed even better)

And things like GSQ-RCO optimization from ISTA labs, EXL that you linked. Preserving bits for certain (large) domains like agentic coding. Pruning others. It really works and creates functional models.