Reasoning models are trained from non-reasoning models of the same scale, and the training data is the output of the same model, filtered through a verifier. Generating intermediate context to improve the final output is not an idea that reasoning models are based on, but an outcome of the training process. Because empirically it does produce answers that pass the verifier more often if it generates the intermediate steps first.

That the model still makes mistakes doesn't mean it's not an improvement: the non-reasoning base model makes even more mistakes when it tries to skip straight to the answer.