You just don't "learn reality" by getting good at representations. You can learn a data set. You can learn a statistical regularity in things such as human languages. You can analyze the concept spaces of LLM's and compare them numerically. I agree with that.

What the hell does "learning an objective shared reality" mean?

This reminds me of EY saying that a solomonoff inductor would learn all of physics in a few days of a 1920x1080 data stream. Either it's false (because it needs to do empirical testing itself), or it's true, but only if you presuppose the idea that it has a perfect model of all the interactions of the world and can decide between all theories a priori... so then why are we even asking if it's a "perfect learner"? It already has a model for all possible interactions already, there's nothing out of distribution. You might argue, "Well, which model is the correct one?" That's the wrong question already - empirical data is often about learning what you didn't know that you didn't know, not just learning about in-distribution unknowns.

I just get an ick because I associate people talking about this hypothesis as if "LLM's converge on shared objective reality => they are super smart and objective, unlike humans". LLM's can be smart. They can even be smarter than humans. It's also true that empiricism is king.

Potentially useful for things like innate mathematical operation primitives. A major part of what makes it hard to imbue LLMs with better circuits is that we don't know how to connect them to the model internally, in a way that the model can learn to leverage.

Having an "in" on broadly compatible representations might make things like this easier to pull off.

This proves a decimal system is correct. Base twelve numeral systems are clearly unnatural and inefficient.