Did they maybe expand to a tree during training to learn more robust reasoning? Maybe. But it still comes down to a regular transformer model at inference time.
https://arxiv.org/pdf/2403.09629
> In the Self-Taught Reasoner (STaR, Zelikman et al. 2022), useful thinking is learned by inferring rationales from few-shot examples in question-answering and learning from those that lead to a correct answer. This is a highly constrained setting – ideally, a language model could instead learn to infer unstated rationales in arbitrary text. We present Quiet-STaR, a generalization of STaR in which LMs learn to generate rationales at each token to explain future text, improving their predictions.
>[...]
>We generate thoughts, in parallel, following all tokens in the text (think). The model produces a mixture of its next-token predictions with and without a thought (talk). We apply REINFORCE, as in STaR, to increase the likelihood of thoughts that help the model predict future text while discarding thoughts that make the future text less likely (learn).
e: answer to my own question https://x.com/_xjdr/status/1835352391648158189
You don't see the scaling with respect to token length with non-FT'd CoT like this, in my opinion.
I'm just saying whatever they did in their [new] model, I think they also added CoT on top of it, as the outer layer of the onion so to speak.
I've gotten mini to think harder by asking it to, but it didn't make a better answer. Though now I've run out of usage limits for both of them so can't try any more…
nobody except for recent deepmind research has shown test time scaling like o1