alt Hacker NewsChain-of-thought output shouldn't be taken literally. The tokens are a substrate for computation, not necessarily evidence that the model is wasting time and electricity by gratuitously second-guessing itself over and over.
You can see evidence of this phenomenon in models dating back to the OG Deepseek R1. It was common to see the model talk itself out of the correct solution in the <thinking> block, or fail to reach it at all, only to produce a correct answer in the response. And vice versa; it was also common to see it reason its way to the right answer and then fail to follow through in the response.
I am interested however in why fine-tuning on reasoning traces of a frontier model is such an effective way of improving an (open-weight) base model. See e.g. https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6...
I can see the reasoning being a substrate for computation, but in which space should we interpret this computation to be happening? The vector representations of individual tokens are completely different (and even the way the reasoning traces are broken up into tokens will be pretty different) between Qwen and Claude e.g.. The only way I can see this being effective (which it is) is thus that we SHOULD interpret the model to be "computing" in natural language and thus we can indeed take the chain-of-though somewhat literally.
The Deepseek R1 behaviour you describe is from a model from january last year, are you sure this is not pathological behaviour rather than an indication of the reasoning not needing to be taken literally?
I do however agree with the point that it is not necessarily a dead-end. That Qwen loops almost at an OCD-like level, but retains accuracy on the times it does answer, shows that. Yes ideally it loops less, but I am for now happy to accept that this is what it takes to run models locally. At least it is available for our inspection.