# LightThinker: Thinking Step-by-Step Compression

Authors: Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang.

Compresses segments of a generated reasoning chain into dense states while retaining a stepwise inference process.

First public: 2025-02-21.
ArXiv initial submission: 2025-02-21.

[Original paper](https://arxiv.org/abs/2502.15589) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2502.15589/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2502.15589/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2502.15589/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2502.15589/record.json)

## Approaches

- [Internalized reasoning](https://latent-implicit-thinking.github.io/topics/internalized-reasoning/): Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.
- [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.

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.