# Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking

Authors: Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang.

Routes selected tokens through extra computation while reusing model parameters. Marks successive thinking steps so the model can distinguish one pass from the next.

First public: 2025-02-19.
ArXiv initial submission: 2025-02-19.

[Original paper](https://arxiv.org/abs/2502.13842) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2502.13842/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2502.13842/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2502.13842/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2502.13842/record.json)

## Approaches

- [Recurrent reasoning](https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/): Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.

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.