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- Hacker News
- I don’t think next token prediction is a particularly good description of pretraining either.
The intermediate representations at each position are being optimised not only to help predict the next token, but also to help predict all subsequent tokens within the training context.
You can see this directly in backpropagation: the gradient reaching a representation at position i sums contributions from prediction losses for subsequent tokens, not just from the loss for token i+1.
by imenani - The errors LLMs typically made for me were looking for "schmutz" as a jiddish word, got "schmuck" on my screen. Thought why the stupid mistake? The next-token predictor model perfectly explains it.
Or starting with "yes". And this early locking in was a total lie, in the discussion that became "yes, might appear that way, but totally no since reasons". So it should have written "No", topmost, but could not self-edit that.
But nice that this gives it a more nuanced view, I might have to update my priors.
by a3w - Sorry, I'm not convinced. "make_more_likely" is always maximising the probability of the next token given a sequence of preceding tokens. That's what we mean when we say "next token prediction", that the model returns the token that's most likely to follow the current sequence of tokens. The mechanism used to do that doesn't matter, it's still predicting the next token whether that's because it maximises a reward or because it follows a gradient or whatever else one might think.
Btw it's "make_most_likely" not "make_more_likely". It's just that "most" gets "mosted more" with every pass through the training data.
Edit: the article author's argument is that RLVR is different because it's learning to predict the next token by generating its own token sequences. That makes no difference: what is learned is still the conditional distribution P(token_k|tokens(1,...,k-1). That's the prediction task. Doesn't make a difference where you learned it from or how.
- It's a weird article. Despite the title and some of the text, much of the article makes the point that LLMs are next-token predictors, where the predictions are based on both training data and various reinforcement learning techniques.by jmull
- Obfuscation is the goal of the hype cycle in VC. Certain firms & individuals are minting money and that’s all that matters to them.
That there’s a legion of LLM nerds arguing deterministic this, pretraining & rewards that all the better for the con job they’re pulling off.
The technology will be relegated to the trash bin of history, just like crypto.
by grebc - “LLMs are just X”, mostly with X = “next token predictors” is a common pattern to dismiss the power of AI with a very shallow understanding of how they really work.
It’s not wrong, but because LLMs are generators, and generation is a kind of prediction. And current mainstream models are autoregressive, which means they generate things one by one in order. But these trivia doesn’t tell us anything interesting about how they work or their limits.
It’s like saying a Boeing 777 is just a rotating machine, and it flies by just rotating some fins. Well yes, but no. With that level of simplification we’ve just ignored 150 tons of advanced engineering and physics. Similarly with token generator simplification we ignore a few trillion parameter Transformer. That transformer is more complex than a Boeing 777, and we don’t really know how it works.
A tiny ML model can do “next token prediction”. This is not as simple as that.
by ozgung - The whole point of transformers is that you can take “a tiny ML model” and just scale it up 100000x and then it tells Zuckerberg what to bake with his kidby wonnage
- It's not simple to do next token prediction. That IS what is going on. You want something 'deep'. Deep things are often very not complicated.
The deep realization is that if you can predict the next token well enough, you can do things like this:
<paste the first 10 chapters of a mystery novel>. And it turned out the killer was
And if it's really good at predicting the next token, it has to understand the novel and the clues, which means understanding the context and the language and human norms and innuendo and story telling, and tropes, and red herrings, and predict who the killer was.
I think you want it to be something more complicated. It's literally not. It just turns out predicting the next token is equivalent to a universal compression algorithm, which is a form of general intelligence. And we have almost unlimited 'labeled' data to train autocomplete.
by ltbarcly3 - 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.
by mofeien - 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
by wonnage - 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?
by zahlman - > 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.
by Planktonne - A better statement might be:
Modern agentic work is probably more of a "emergent system from simple rules and complex interactions" paradigm than a genuinely new technology.Current agentic systems may be *built* from next-token predictors which are conceptually simple, but because of agentic frameworks, recursive invocation, tool use, and *heavy* investment in reinforcement learning in these contexts and for specific applications, they can no longer be thought of as "Merely" next token predictors.by jvanderbot - Nope. Post training means even the raw model isn't predicting.
- the next token part is like the "clock" that drives itby whateveracct
- It’s written in Claudish, or perhaps a human who has been reading too much Claudish recently. I am starting to become allergic to Claudish. Not there fully yet — but it’s at a tipping point genuinely worth exploring and worth being precise about.by teekert
- > or perhaps a human who has been reading too much Claudish recently
To be fair, is there anyone who hasn't been "reading too much Claudish recently" who is also qualified to write on the topic?
by zahlman - > Calling the second system a “next-move predictor” would be strange. It is not trying to predict what move appeared next in a dataset. It is trying to choose a move that wins.
i dont understand the distinction here. does working backwards from a set of win states instead of working forwards from the current state somehow change whether it’s a prediction or not?
by stanleykm - The word "predict" has a meaning. I don't "predict" my next move in chess. I might predict what someone elses first move is.
- In the article I made 3 claims, and I agree it was a bit clumsy.
1st I say that "working forwards" in the sense of outputting one token at a time could be some form of prediction, I don't argue against that. This is what LLMs do at inference time.
2nd I say that to me what really constitutes a prediction is the pre-training. Here it's the classic setting for the word prediction in ML. The model outputs a prediction of the ground truth label: the next token.
3rd I argue that in RL there is no ground truth next token, so prediction doesn't apply here anymore.
Back to your question then: you're asking points 3 and 1 are different. Working backwards from a set of win states is basically what RL does in training. Working forward from the current state is what inference does. To me there is a distinction worth thinking about. First between the mechanism at inference time and at train time. Then between what happens in pre-training vs. RL post training.
by garrinm - The distinction is that it's not 'predicting the next token'. Instead it's _determining_ the next token based on a prediction of its reward signal.by Veedrac
- I'm not going to stop describing things accurately because someone generated an article that continually undermines its own main point. Limiting the way we talk and think about LLMs to a very narrow set of terms doesn't help us.
EDIT: gentler phrasing
by Planktonne - >> EDIT: gentler phrasing
Thank you.
- > generated an article that continually undermines its own main point.
I disagree that this accurately describes TFA.
by zahlman - The distinction I perhaps didn’t make clearly enough is that I’m not really debating the concept of prediction at inference time, although, as I pointed out elsewhere, I think that’s the less interesting interpretation of what “prediction” means.
What’s more interesting to me is its application at training time. In reinforcement learning, there is no ground-truth next token to predict.
So if you’re comfortable calling Deep Blue a “next move predictor,” then I think it’s perfectly consistent to call an LLM a “next token predictor.” But I think it’s more useful to think of Deep Blue as evaluating the value of possible moves. roughly, how likely they are to lead to winning.
And I think effectively the same distinction applies here.
by garrinm - 'next token predictor' is a limited mental model but it's actually much better than any others.
'pattern matching' is a better intuition that 'reasoning' even though I think nominally, using the term 'reasoning' is perfectly fine in that context. It's just a loaded word that brings too much to the table.
'It hasn't seen the pattern' is a better description of the limitations of AI because it really just doesn't generalize very well at all. The adaptations described in the article don't change that.
Those are mutations, not expansions of capability.
by bluegatty - How about "outcome steering" as a mental model? During training it is optimized until it's really successful at producing code / terminal commands / words that make the compiler/computer/itself do something that ultimately completes a long time-horizon task that iswcurrently being trained.by mofeien
- >> 'pattern matching' is a better intuition that 'reasoning' even though I think nominally, using the term 'reasoning' is perfectly fine in that context. It's just a loaded word that brings too much to the table.
Can I be a little pedantic? It's "pattern recognition" not "pattern matching". "Pattern matching" is what we do when we write a regex to recognise a sub-string in a bit of text. "Pattern recognition" is a subfield of AI that studies how to find patterns in data. For example the outcome of "pattern recognition" could conceivably be a regex that fits a large number of substrings in some corpus of text (essentially a regular grammar used to compress the text maybe). A regex is already a pattern, usually formed by a human (or an LLM these days) while pattern recognition starts without any patterns and builds them up from looking at the data.
It's harder to pin down "pattern recognition" because it's an ancient term that was once its own field, before it got absorbed into modern machine learning, and because there are countless approaches to it, quite unlike "pattern matching" which is basically just regexes plus a couple of other rarer things (like unification).
- 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.
- 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.