No. AI learns to predict reasons, and doing so as it predicts the answer improves its accuracy at predicting the answer.
In summary, even though they are called "reasoning" models, they are still based on prediction and pattern matching, not true logical reasoning. The improvement in accuracy is likely due to better leveraging of the model's statistical knowledge, rather than any deeper understanding of the problem's logic. And the reasons you see it output have nothing to do with the actual reasons it used to determine the answer.
In fact, R1.Zero hints that, it might be even better to let the AI follow a chain of thought that doesn't actually make logical sense or is understandable, and that doing so could even further improve its ability to accurately predict solutions to code, math and logic problems.
In summary, even though they are called "reasoning" models, they are still based on prediction and pattern matching, not true logical reasoning. The improvement in accuracy is likely due to better leveraging of the model's statistical knowledge, rather than any deeper understanding of the problem's logic. And the reasons you see it output have nothing to do with the actual reasons it used to determine the answer.
In fact, R1.Zero hints that, it might be even better to let the AI follow a chain of thought that doesn't actually make logical sense or is understandable, and that doing so could even further improve its ability to accurately predict solutions to code, math and logic problems.