I suppose it's because LLM training data uses text that can contain reasoning within it, but without any specific context to specifically learn reasoning. I feel like the little reasoning an LLM can do is a byproduct of the training data.

Does seem more realistic to train something not on text but on actual reasoning/logic concepts and use that along with other models for something more general purpose. LLMs should really only be used to turn "thoughts" into text and to receive instructions, not to do the actual reasoning.