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.
Not the person you replied to, but I think a more accurate description of the reasoning we see is proof of effort, not necessarily great insight into how the reasoning is occurring. For the most part, researchers currently describe the intent and motives (in however one may define them for LLMs) as black boxes right now. Even the mechanics of the cognitive process is not well understood. Depending on the model and harness, the thinking will often look like gibberish. I suspect they've invested considerable effort into presenting thinking as a reasonable approximation of what they imagine it to be. Claude and OpenAI have also begun encouraging multi-step problem solving (or the models themselves decide this), and we can see their more accurate responses at the conclusion of each phase.
Fine tuning or post-training is effectively biasing certain outcomes: making them more likely to occur. This comes with trade-offs. A coding LLM will bias technical language, which would harm a model for general use.
This opens a really interesting field of research. Our brains use specialised regions because specialisation turned out to be the most energy efficient method for biological compute. It might also be the best performant. We don't want to activate 100% of our prefrontal cortex to breath. What a stupendous waste of the organ. I think we see incredible advancements in model clusters in the future, using specialised models for specialised tasks. We have the appearance of this today in some harnesses, but they are shallow imitations. The real innovation will be low-cost, accurate routing. Existing solutions are woefully inadequate for many reasons.