Because reasoning is an emergent byproduct of training it on all knowledge. It still doesn't "know" things in this form and just generates tokens, no matter how weird we spin it.

So if you don't train it on a large dataset of a lot of words with a lot of sensible connections, it won't be able to reason, as it won't be able to make proper connections between words and sentences.

You can try training a really small model and seeing the gibberish outputs when you train it on only a small dataset.

Minmaxing the dataset to extract maximum generation with minimal data does sound like fun, but if you want to build SoTA models as a company, the economic tradeoff of doing that vs slapping a few more GPU's together is terrible.