alt Hacker NewsAt this point it's either wrong or meaningless to call LLMs "next token predictors". Yes, pretraining involves predicting the next token. But after that you're in the world of RLHF, RLVR, and other post-training.
A model is _sort of_ designed to predict the next token in a string of text. But it's equally accurate to say it's designed to edit code until it passes a suite of unit tests.
Yes, they generate sequentially, so "next token predictor" isn't technically incorrect (which is why I chose the word "meaningless"). But it's pretty clear there's a mechanism inside them that allows them to "plan" what they generate. I might as well call you a next word predictor.
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Yes, I didn’t mean that literally. Since “attention is all you need” it’s more like “predict the full output in full for this blob of input text”. That doesn’t change the “wtf is going on with the weight understanding bit” in what I wrote.
Its a stateless function that takes in x amount of words and calculates logits. It is in fact a next token predictor. Its literally nothing more than that.
It is not clear that there is a mechanism that allows them to plan anything. It is clear there is nothing of the sort.
You can demonstrate definitively that there nothing other than "next token prediction" by running the same inference engine/model/prompt/settings multiple times in a row on the same hardware at temp 0 with a seed set.
If you turn the temp up, then you might let the "mechanism" select pseudorandomly from a probability distribution of k-likely tokens with minimum p-cumulative probability. Even then, if the next token has a very high probability, deviation will be uncommon.
I just performed this experiment right now on a few dozen runs of a dense model at thousands of tokens of output at temp 0, and all were identical. Then I did it with a MOE model, which might get some benefit of picking different experts each time. Nope. Outputs were again identical.
My understanding is that the underlying part is a next token predictor and that's what is "actually" editing the code but the part that decides what to do (to achieve a successful unit test result for example) is a mix of all the things including post training.