I think you could probably train a model to consider boolean logic, modal logic, and mathematics reasonably well, but there is still a pretty big leap between that and thinking about things.

Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box.

Requires knowledge of things not mentioned in the question (notably gravity).

Strict definition of all terms quickly gets you into a quagmire of complexity. Some base level of knowledge about things is required for you to give it instructions. If it only knows how to reason, it lacks any idea of what to aim to achieve.

There is quite a pronounced disconnect between the vast stores of written data that models are trained on and robust consideration of a topic. I do wonder if the path can be directed by the order of training.

For example if you train a model to basic literacy using tinystories, then math and philosopy texts, then psychology, and sociology texts, and then finally the mass data of everything from conversations and rants, to code and fiction.

Does that end up with a significantly different model to one that is trained on books on acting, creative writing, and fantasy novels, before introducing the same final mass data set.

How much does it's current ability allow it to contextualise new training data?