I’m not sure which passage you seem to refer to, saying that my argument implies this. The sections “The Bandwidth Intuition/Counterargument” are supposed to clear exactly this, stating roughly that I understand that there is still obviously a loss of information and as such it’s nonsensical for a normal NN to have miniscule layers. This isn’t an accurate assessment for neuralese LLMs though. They recursively aggregate this error, turning it into a lot bigger problem. If simply allowed to grow through the tens and hundreds of forward passes, it’s simply not worth it. If we do already tokenize them though, quantization no longer would serve any real purpose.
I hope my position has become a little bit more clear.
I’m not claiming I know the perfect cut off point between not losing information and not letting errors accumulate, if something like that even exists. It could very well be that after 3 forward passes with neuralese, it would still be mostly fine or it could also be that even in the middle of a single forward pass it makes sense to have some kind of mitigations (I think you could build an argument around MoE being a mitigation). But what this perfect ratio is doesn’t really matter, the point is that recurrent forward passes will be a thousand times worse than a normal forward pass and therefore can’t be worth it anymore.
Making everything discrete is one extreme just as making everything continuous is the other extreme. I’m arguing that the golden ratio lies somewhere in the middle, recognizing the importance of both rich, continuous representations and clear, discrete representations.
I’m not sure which passage you seem to refer to, saying that my argument implies this. The sections “The Bandwidth Intuition/Counterargument” are supposed to clear exactly this, stating roughly that I understand that there is still obviously a loss of information and as such it’s nonsensical for a normal NN to have miniscule layers. This isn’t an accurate assessment for neuralese LLMs though. They recursively aggregate this error, turning it into a lot bigger problem. If simply allowed to grow through the tens and hundreds of forward passes, it’s simply not worth it. If we do already tokenize them though, quantization no longer would serve any real purpose.
I hope my position has become a little bit more clear.
But why would this error accumulation be a problem in recurrent forward passes and not one long forward pass?
I’m not claiming I know the perfect cut off point between not losing information and not letting errors accumulate, if something like that even exists. It could very well be that after 3 forward passes with neuralese, it would still be mostly fine or it could also be that even in the middle of a single forward pass it makes sense to have some kind of mitigations (I think you could build an argument around MoE being a mitigation). But what this perfect ratio is doesn’t really matter, the point is that recurrent forward passes will be a thousand times worse than a normal forward pass and therefore can’t be worth it anymore.
Making everything discrete is one extreme just as making everything continuous is the other extreme. I’m arguing that the golden ratio lies somewhere in the middle, recognizing the importance of both rich, continuous representations and clear, discrete representations.