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Comment by mofeien | Hacker News Reader

Describing it as a "next-token predictor" in the sense that this would mean it's fundamentally limited to just a fraction of an inferential step is doubly wrong:

1. In order to select even the first word of a meaningful sentence, it already has to have structure and meaning of what follows captured somewhere inside, mostly in it's weights/activations or indexed by it's state vector.

2. What you see when you use an LLM is not next-token prediction directly next to the prompt, but instead following a block of varying length of next-token prediction that happened to make progress on the problem in your prompt, and which just summarizes the results.

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> Describing it as a "next-token predictor" in the sense that this would mean it's fundamentally limited to just a fraction of an inferential step

I don't think anyone is doing that though; we know LLMs are not simple Markov chains, and that the prediction they make is based on more than the previous X words.

It's not minimising to describe even a complex prediction process as prediction.

But it literally is making a prediction based on the previous X tokens, it's just that X is huge and there is a proportionally huge number of parameters in the token generation function.

The discourse around this is annoying. A big group of people use "next-token predictor" to imply that LLMs aren't capable of anything interesting. Another big group of people opposes the use of "next-token predictor" because of that implication. But that fight isn't about the "predictor" language at all.

The linked article makes a good point: a substantial chunk of the training does not consist of "here's a bunch of tokens, here's the next token, learn that." But all the comments want to turn it into a referendum on the goodness of AI.

The fight is about the predictor language in some cases. Because it's only a trivial difference to those who don't understand the details of how these things are made. In pre-training the model really is trained to predict the next token. What is being emitted by the model is, by structure, by training and by optimization, a prediction of the very next token.

What is emitted by a model during RLHF and RLVR is not, by structure, training or optimization, a prediction of the next token.

Right. But that's never what the discussion is about. This is the first time I've seen someone say, "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token." I have seen many, many people say "it's not just a next-token predictor because there's all this smart stuff going on inside the model." Even here, in the comments for an article that's all about the former, most of the objections to "next-token predictor" are of the latter form.

Yup, you are mostly right.

I guess the people in my camp find the "it's just a next token predictor" stupid in that it's like saying "it's just a bunch of carbon and hydrogen", but it's also one of those things where people like to think they are clever because they think they are theoretically correct. But they aren't even that. So it's like double stupid. But the "next token predictor" part is at least technically correct (like, carbon and hydrogen right) for pretraining, so the debate can't really be won there.

they are cleverly arranged / configured next-most-likely-token predictors, possibly with some clever procedures / attachments on top.

Nope. This isn't right.

> "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token."

clever procedures on top of the base transformer architecture.

i used simplified words/phrases to summarise the same thing you two were saying (the intent being: here's a version that may be digestible when discussing with others).

apparently that means i'm wrong though, no idea why because it seems you've decided to be dismissive rather than constructively elaborate on why this simplified and digestible version might be wrong :shrug:

They aren't predicting the next token. It's quite literally not a prediction.

They're estimating a probability distribution over the next token, from which a sample is taken. Close enough.

It's not an estimation of something. It's a policy.

Sure, you're right.

But it's a policy learned from a next-token prediction task. You could also call it an inferrer or generator or whatever. The point is that it takes as input a sequence of preceding tokens and emits one more token to continue the sequence.

The policy is not learned token by token during RLHF and RLVR. The reward model doesn't score token by token.

So? tokens are emitted one at a time according to the output distribution & sampler algorithm, and the next token distribution is a function of the preceding token sequence only. The process by which the output distribution is shaped doesn't change the core mental model, and doesn't reduce its value. It's a prediction in the jargonic sense that an inference about future values of a time series is broadly called "prediction", and it's relevant for reasoning about LLMs because they are fundamentally limited to converting tokens sequences into next-token predictive distributions, and that bears on how they can do what they do and what their limitations are. Nothing about the training process changes that.

We're talking to a RLVR bot.

Bad bot.

It really is an important distinction, though. Being a next token predictor doesn't stop it from writing good sentences, but it does mean an LLM by itself can't play the number guessing game with you.

This is pedantic, but, actually RL has improved the quality of sentence construction in LLMs quite dramatically... And once you do some RL on that model, it aint a next token prediction machine any longer.

That's just training, isn't it? (Even if the term is "post-training") It doesn't change how inference works.

My understanding is it usually strengthens the "thinking ahead" part of inference, but that part was already there, and it's still at the end of the day picking one token and then purging internal state in a way that can only partially be recovered from.

How inference works is you do a bunch of math and token weights come out, and then you pick one based on some sampling policy. “Prediction” is really about how they’re trained than how they run afterwards.

The discussion is basically: what is a model trying to do?

One may reasonably assert it isn't trying to do anything. But, in practice, if you give it an objective function and optimize it, the model is basically trained to "do" something. So what is it trained to "do"? During pre training it is trained to produce a distribution which is a prediction of the next token in it's training data samples. During RLVR and RLHF, it is trained to produce a distribution of tokens that will maximize a scoring function over many steps - not just the next step. The fact that it produces a distribution of potential choices for the next step doesn't mean the next step is a prediction. It's more of a "strategy" or "probabilistic path choice". The word used in RL is a "policy". It's a decent word to describe what the model is.

So, modern LLMs are trying to produce a good sequence of tokens. They are "good token sequence producer machines". Not "next token prediction machines". Pre RLHF (in practice, go back to pre chatgpt) they really were "next token prediction machines".

You can pour a bunch of beads into a pachinko board and end up with a normal distribution. Do the system “know” they need to go there?

Then you feed a bunch of tokens into a GPU and end up with a distribution of possible next tokens…

If you are going to say "literally", then what is your literal definition for the word "prediction" ?

It has nothing to do with X being huge. In fact X might be quite small.

Even so, one might wonder why we don't try making systems that take different approaches. For example, after a traditional first pass of output, they could do sliding-window "optimizations" considering each token in the context of tokens both before and after, and possibly replace words or phrases in-place.

For example, I've noticed quite a few cases recently of LLMs outputting "but" where "and" would make more sense, or vice-versa. Surely that could be improved by such an approach?

Look up diffusion models.

Indeed; but I've only heard of them being used for images rather than text. Why?

People have and are trying things. Lots and lots of things. They just don't go around promoting failed ideas.

I think you are just litigating the philosophical debate of Bayesian vs frequentist interpretations of probability. Because the weights really are just probabilities.

Bayesians say that the probabilities represent strength of belief, implying some subjective knowledge or information. It is necessarily subjective in that it requires priors, i.e information the predictor knew before making the prediction. In other words, the LLM has priors from training and is predicting tokens using real knowledge

Frequentists would say that probabilities are simply objective facts - e.g we all agree that the physical property of temperature follows from any molecules matching a particular energy distribution. You’re not predicting anything, there’s just some outcomes that are happening at the expected rate. In other words, the LLM is a stochastic parrot/next token predictor

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