“Next-token predictor” is the wrong mental model for LLMs
gmcgoldr.github.io73 points by garrinm 8 hours ago
73 points by garrinm 8 hours ago
To be honest, I believe I get the point the article is trying to make, and to an extent I agree, but I also think the point is not really made very well.
The core of the argument as I understood it is that LLMs aren't just using existing data is training but also new ones. That's fine and good, and you can't simply assume an LLM is simply mashing together all it's data to give you an average of all that got fed into it - but at least I would still call it a "next token predictor"
It's not using just training data, but what it's doing is predicting the next token to get to the solution. As far as my amateur knowledge goes, LLMs still roughly go token by token, deciding which one fits best given the context.
It's just not predicting based on it's training data, but predicting based on RLVR & more, trying to get to the optimal solution ( as much as the solutions CAN be optimal)
And I honestly think keeping this very much in mind is helpful in understanding and dealing with LLMs.
Yeah, it sounds like this is just a disagreement about what the word "next" means. I agree with you that "next" just means "the one about to come", and if the underlying model works by using some prediction mechanism to determine that, then it's by definition a next-token predictor. Disagreeing with that on the basis that the "next" token isn't necessarily in the training data verbatim just seems like an overly strict definition of the word "next".
I think it’s a disagreement about what ‘predict’ means.
The OP is arguing against people who think that an LLM is ‘predicting’ what token would likely follow if the text preceding were found among the corpus it was originally trained on.
Instead it is ‘predicting’ what token would follow if the text were found among really good examples of the text it has being reinforced to produce - be that ‘chats with a helpful assistant’ or ‘sets of changes to a codebase’.
And that isn’t really ‘prediction’, so much as ‘generation’.
It’s not been tuned to ‘guess the next token right’. It’s been tuned to generate the token that leads to it ultimately scoring highest on its reward function.
It’s not predicting the token, it’s predicting the reward.
That seems overly pedantic to me. If I asked you "What's your prediction for the Super Bowl?", I'm pretty confident you would infer that I mean predicting the outcome, not the event itself.
But if you are an NFL coach and I ask you to decide your next action in order to maximize your odds of winning the superbowl, while yes that does involve you having some predictive ability to think about what impact your actions would have on your odds of winning the Super Bowl… I don’t think you would call the process that you use to decide that next action ‘prediction’.
I don't find the fact that I don't call any humans "action predictors" to be a particularly meaningful insight because my rationale is that it's a weird thing to call a human; football coaches can do plenty of other things besides just coaching football.
Yep, we’re all just putting one foot in front of the other, hoping we’re doing the right thing to bring about the outcomes we want, trying our best.
But that’s the point: so is an LLM. Putting one token in front of another, hoping it’s doing the right thing to bring about the rewards it’s trained to… trying its best.
So yeah, not ‘next token predictors’. ‘Next token tryers’ maybe.
yes, it's exactly this. And it's not a trivial distinction.
It’s deterministically trivial.
Post train a model, you'll be able to determine it is not.
The blog post itself says one thing, but then demonstrates the exact thing they’re arguing against.
If you can’t grasp that logic gap then there’s no point discussing further.
Nope, it doesn't.
No logic required, you can just build an LLM yourself, including post training. You'll see that predicting the next token isn't something the model does or is optimized for in RLHF or RLVR. You can hand wave all you like, but you have never done it.
Yes, no logic is necessary for LLM adherents we're all finding out.
Carry on good soldier.