  When writing and talking, people sometimes pause to think. Although
reasoning-focused works have often framed reasoning as a method of answering
questions or completing agentic tasks, reasoning is implicit in almost all
written text. For example, this applies to the steps not stated between the
lines of a proof or to the theory of mind underlying a conversation. 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 address key challenges, including 1) the
computational cost of generating continuations, 2) the fact that the LM does
not initially know how to generate or use internal thoughts, and 3) the need to
predict beyond individual next tokens. To resolve these, we propose a tokenwise
parallel sampling algorithm, using learnable tokens indicating a thought's
start and end, and an extended teacher-forcing technique. Encouragingly,
generated rationales disproportionately help model difficult-to-predict tokens
and improve the LM's ability to directly answer difficult questions. In
particular, after continued pretraining of an LM on a corpus of internet text
with Quiet-STaR, we find zero-shot improvements on GSM8K
(5.9%$\rightarrow$10.9%) and CommonsenseQA (36.3%$\rightarrow$47.2%) and
observe a perplexity improvement of difficult tokens in natural text.
Crucially, these improvements require no fine-tuning on these tasks. Quiet-STaR
marks a step towards LMs that can learn to reason in a more general and
scalable way.
