> 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).
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).