# Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Authors: Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, Noah D. Goodman.

Trains on ordinary text by generating written rationales before predicting what comes next. Learns from how much those rationales improve subsequent token predictions.

First public: 2024-03-14.
ArXiv initial submission: 2024-03-14.

[Original paper](https://arxiv.org/abs/2403.09629) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2403.09629/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2403.09629/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2403.09629/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2403.09629/record.json)

## Approaches

- [Thinking tokens](https://latent-implicit-thinking.github.io/topics/thinking-tokens/): Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.