# Learning When to Think Aloud in Latent Reasoning

Authors: Zhehao Zhou, Tongrui Su, Shengyu Zhu, Da Li, Zeyu Liu, Jun Zhu, Xueqi Cheng.

Learns when to write an intermediate token and when to feed a hidden state back into the model. Retains important words from each reasoning step as training anchors, then trains the mixed text-and-state policy with answer and length rewards.

First public: 2026-11-04.
ArXiv initial submission: 2026-11-04.

[Original paper](https://arxiv.org/abs/2611.06108) · [PDF](https://arxiv.org/pdf/2611.06108) · [HTML](https://arxiv.org/html/2611.06108v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2611.06108/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2611.06108/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2611.06108/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2611.06108/record.json)

## Approaches

- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.

Reading-list version 0.15.2; literature checked through 2026-11-03.