S1: A $6 R1 competitor?

I found the discussion around inference scaling with the 'Wait' hack so surreal. The fact such an ingeniously simple method can impact performance makes me wonder how many low-hanging fruit we're still missing. So weird to think that improvements on a branch of computer science is boiling down to conjuring the right incantation words, how you even change your mindset to start thinking this way?

I think the fact alone that distillation and quantization are techniques that can produce substantial improvements is a strong sign that we still have no real comprehensive understanding how the models work.

If we had, there would be no reason to train a model with more parameters than are strictly necessary to represent the space's semantic structure. But then it should be impossible for distilled models with less parameters to come close to the performance of the original model.

Yet this is what happens - the distilled or quantized models often come very close to the original model.

So I think there are still many low-hanging fruits to pick.

> still have no real comprehensive understanding how the models work.

We do understand how they work, we just have not optimised their usage.

For example someone who has a good general understanding of how an ICE or EV car works. Even if the user interface is very unfamiliar, they can figure out how to drive any car within a couple of minutes.

But that does not mean they can race a car, drift a car or drive a car on challenging terrain even if the car is physically capable of all these things.

We know how the next token is selected, but not why doing that repeatedly brings all the capabilities it does. We really don't understand how the emergent behaviours emerge.

It feels less like a word prediction algorithm and more like a world model compression algorithm. Maybe we tried to create one and accidentaly created the other?