Technically it is not a reasoning machine. If it was a reasoning machine then we would not see results like this:

> To systematically investigate the role of end-user semantics of derivational traces, we set up a controlled study where we train transformer models from scratch on formally verifiable reasoning traces and the solutions they lead to. We notice that, despite gains over the solution-only baseline, models trained on entirely correct traces can still produce invalid reasoning traces even when arriving at correct solutions. More interestingly, our experiments also show that models trained on corrupted traces, whose intermediate reasoning steps bear no relation to the problem they accompany, perform similarly to those trained on correct ones, and even generalize better on out-of-distribution tasks.

https://arxiv.org/abs/2505.13775

Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens