Comment by tovej
19 days ago
They do not model the world.
They present a statistical model of an existing corpus of text.
If this existing corpus includes useful information it can regurgitate that.
It cannot, however, synthesize new facts by combining information from this corpus.
The strongest thing you could feasibly claim is that the corpus itself models the world, and that the LLM is a surrogate for that model. But this is not true either. The corpus of human produced text is messy, containing mistakes, contradictions, and propaganda; it has to be interpreted by someone with an actual world model (a human) in order for it to be applied to any scrnario; your typical corpus is also biased towards internet discussions, the english language, and western prejudices.
If we focus on base models and ignore the tuning steps after that, then LLMs are "just" a token predictor. But we know that pure statistical models aren't very good at this. After all we tried for decades to get Markov chains to generate text, and it always became a mess after a couple of words. If you tried to come up with the best way to actually predict the next token, a world model seems like an incredibly strong component. If you know what the sentence so far means, and how it relates to the world, human perception of the world and human knowledge, that makes guessing the next word/token much more reliable than just looking at statistical distributions.
The bet OpenAI has made is that if this is the optimal final form, then given enough data and training, gradient descent will eventually build it. And I don't think that's entirely unreasonable, even if we haven't quite reached that point yet. The issues are more in how language is an imperfect description of the world. LLMs seems to be able to navigate the mistakes, contradictions and propaganda with some success, but fail at things like spatial awareness. That's why OpenAI is pushing image models and 3d world models, despite making very little money from them: they are working towards LLMs with more complete world models unchained by language
I'm not sure if they are on the right track, but from a theoretical point I don't see an inherent fault
There's plenty of faults in this idea.
First, the subjectivity of language.
1) People only speak or write down information that needs to be added to a base "world model" that a listener or receiver already has. This context is extremely important to any form of communication and is entirely missing when you train a pure language model. The subjective experience required to parse the text is missing.
2) When people produce text, there is always a motive to do so which influences the contents of the text. This subjective information component of producing the text is interpreted no different from any "world model" information.
A world model should be as objective as possible. Using language, the most subjective form of information is a bad fit.
The other issue in this argument is that you're inverting the implication. You say an accurate world model will produce the best word model, but then suddenly this is used to imply that any good word model is a useful world model. This does not compute.
> People only speak or write down information that needs to be added to a base "world model" that a listener or receiver already has
Which companies try to address with image, video and 3d world capabilities, to add that missing context. "Video generation as world simulators" is what OpenAI once called it
> When people produce text, there is always a motive to do so which influences the contents of the text. This subjective information component of producing the text is interpreted no different from any "world model" information.
Obviously you need not only a model of the world, but also of the messenger, so you can understand how subjective information relates to the speaker and the world. Similar to what humans do
> The other issue in this argument is that you're inverting the implication. You say an accurate world model will produce the best word model, but then suddenly this is used to imply that any good word model is a useful world model. This does not compute
The argument is that training neural networks with gradient descent is a universal optimizer. It will always try to find weights for the neural network that cause it to produce the "best" results on your training data, in the constraints of your architecture, training time, random chance, etc. If you give it training data that is best solved by learning basic math, with a neural architecture that is capable of learning basic math, gradient descent will teach your model basic math. Give it enough training data that is best solved with a solution that involves building a world model, and a neural network that is capable of encoding this, then gradient descent will eventually create a world model.
Of course in reality this is not simple. Gradient descent loves to "cheat" and find unexpected shortcuts that apply to your training data but don't generalize. Just because it should be principally possible doesn't mean it's easy, but it's at least a path that can be monetized along the way, and for the moment seems to have captivated investors
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> It cannot, however, synthesize new facts by combining information from this corpus.
That would be like saying studying mathematics can't lead to someone discovering new things in mathematics.
Nothing would ever be "novel" if studying the existing knowledge could not lead to novel solutions.
GPT 5.2 Thinking is solving Erdős Problems that had no prior solution - with a proof.
The Erdos problem was solved by interacting with a formal proof tool, and the problem was trivial. I also don't recall if this was the problem someone had already solved prior but not reported, but that does not matter.
The point is that the LLM did not model maths to do this, made calls to a formal proof tool that did model maths, and was essentially working as the step function to a search algorithm, iterating until it found the zero in the function.
That's clever use of the LLM as a component in a search algorithm, but the secret sauce here is not the LLM but the middleware that operated both the LLM and the formal proof tool.
That middleware was the search tool that a human used to find the solution.
This is not the same as a synthesis of information from the corpus of text.
Are we sure? Why can't the LLM use tools, run experiments, and create new facts like humans?
Then the LLM is not actually modelling the world, but using other tools that do.
The LLM is not the main component in such a system.
So do we expect real world models to just regurgitate new facts from their training data?
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they do model the world. Watch Noble price winner Hinton or let's admit that this is more of a religious question then the technical.
They model the part of the world that (linguistic models of the world posted on the internet) try to model. But what is posted on the internet is not IRL. So, to be glib: LLMs trained on the internet do not model IRL, they model talking about IRL.
His point is that human language and the written record is a model of the world, so if you train an LLM you're training a model of a model of the world.
That sounds highly technical if you ask me. People complain if you recompress music or images with lossy codecs, but when an LLM does that suddenly it's religious?
A model of a model of X is a model of X, albeit extra lossy.
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