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".

Maybe I'm wrong - but I see LLMs are a "next-token predictor" as somewhat equivalent to brains are a "bag of molecules".

Both systems have emergent behavior that goes well beyond what would naively be expected.

It is a bit of a pedantic argument but I get upset when people misuse the term, abstraction, and can feel the impulse to write a blog post like this.

I think it’s important to make clear the RL part and the alignment and parameter tweaking that gets done on models and inference engines. It makes them more understandable as mechanisms and less like wish-washy super intelligences that make their own decisions.

When these systems win math Olympiads, it’s not terribly surprising or interesting to me. Of course they will: we trained models to play nearly optimal chess games and Go. You tweak the rewards and sigmoid and you start optimizing the function towards your goal. This is how learning systems of all stripes work.

It’s still next-token predicting at the end of the day. I don’t think it’s a reduction-ad-absurdum.

But a lot of people still call it, “intelligence,” and try to use language that obscures what is happening in terms of anthropomorphic behaviour and not machine ones. That really does influence how we use these tools and profits those who would use them on us.

> 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)

It is predicting based on a model. In many cases we can download the model off hugging face. The model is conditioned by all sorts of things. Training data, post-training, coincidence, prompt inputs, runtime data available from whatever means.

> but at least I would still call it a "next token predictor"

We can call any prediction system a next token predictor. If you watch over the shoulder of a human writing a HN comment you are almost certain to see them generating a linear string of tokens. That is what keyboards do. It is impossible to generate text without being equivalent to a next token predictor.

I think the point is more that in RL there's no ground truth to predict. So when training a model with RL the idea of "predicting" doesn't fit anymore. I'll make some edits I see that I wasn't very clear.

'Prediction' gets overloaded with optimization. Predictions are binary, optimizations are fuzzy.

If you're saying it's predicting, then each result should be falsifiable.

The result of an LLM output should be able to be scored against what it is supposedly predicting. Of course, that isn't possible, because it isn't predicting anything when giving novel outputs, otherwise that thing would exist independently.

Blog articles from Anthropic and others show that this is not true.

A LLM already knows more tokens than the current one. It was mentioned in a blog post about how a LLM is doing haikus and co.

There are also structures in an LLM which allows it to 'estimate' numbers to a certain degree and doing other things.

I just think its a meaningless dismissive term. It literally does predict the next token. But it ignores that it coherently predicts long continuous sequences of those tokens, that tokens can be anything, and you can do almost literally anything with that capability if it does it well enough.

But before ChatGPT,, models had already done that, like all the time, and nobody questioned that these types of models (LLMs) wouldn't be next token predictors, since this is just an inference input data topic. This is questioned only since some less knowledgeable people seem to not have the vocabulary to express emergent properties of large next token predictors.

Reading the article, they wanted to convey that the loss function is more complicated... But they are still next token predictors, just not the trivial ones. Unfortunately, that was true even before, because the input data had to be cleared even decades ago, so there is nothing new. This article just butt hurt that some people deny that there are emergent properties with those, and try to sell something trivial in the field for many decades now. Current LLMs are not different because of these. Also, if they try to sell generated data in the training set, then that's also not new at all.

Some predict multi tokens in one step now.