[fc417fc802]

> Research only showed that thinking might be disconnected from the final output

It is trivial to regularly spot obvious contradictions and inconsistencies if you read carefully. For example I've encountered traces that amounted to "I can deduce X, therefore Y, so that means Z" but then the model turns around and outputs "the answer is W because X". It's even been demonstrated that having the model output placeholder tokens or other gibberish instead of "thoughts" still improves performance. However the thinking traces can still be useful to the end user regardless.

[wolttam]

I see those too and I think of it as the "thinking" in action. If you could replace their actual thinking trace with gibberish and get improved performance that scaled with the amount of gibberish you injected, that's what we'd do. But instead, we see that the quality of of the model's output scales with the amount of 'thinking' tokens they generate before responding.

It has been my experience that yes, models make contradictions throughout their thinking process, but the conclusions they arrive at during/near the end of thinking more often than not align with the final output.