o1's reasoning traces being much terser (sometimes to the point of incomprehensibility) seems predicted by doing gradient updates based on the quality of the final output without letting the raters see the reasoning traces, since this means the optimization pressure exerted on the cognition used for the reasoning traces is almost entirely in the direction of performance, as opposed to human-readability.

In the short term this might be good news for the "faithfulness" of those traces, but what it's faithful to is the model's ontology (hence less human-readable), see e.g. here and here.

In the long term, if you keep doing pretraining on model-generated traces, you might rapidly find yourself in steganography-land, as the pretraining bakes in the previously-externalized cognition into capabilities that the model can deploy in a single forward pass, and anything it externalizes as part of its own chain of thought will be much more compressed (and more alien) than what we see now.