The reasoning in the post is not backwards reasoning, it is literally the information theory of compression, described in human-friendly terms! But I must add t

The reasoning in the post is not backwards reasoning, it is literally the information theory of compression, described in human-friendly terms! But I must add the caveat that the original post is really only saying that it's rather trivial I think that LLMs are learning _some_ world model, not that they learn it very well. But that this must happen in the limit! :D As far as the quality of the world model goes, I think is important. What I'm focusing on in that post at least , I thinks, is that people debate whether or not LLMs even encode a world model, which is such a silly thing to me. Like you brought up, I agree that discussions about the quality of said world model are interesting, and how far whatever [trained XYZ model is] in that rotation towards learning said world model is. I agree that there is a lot of very interesting discussion there! One rough proxy is to measure the different types of variance within the noise of the model, the more high-level and believable it is, the higher-level the representations in the model. But, of course, this is just a rough human friendly proxy. Actually measuring this, maybe is quite difficult! D'''':