# SIM-CoT: Supervised Implicit Chain-of-Thought

Authors: Xilin Wei, Xiaoran Liu, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Jiaqi Wang, Xipeng Qiu, Dahua Lin.

Trains continuous intermediate states with explicit supervision so reasoning can proceed without emitting a full textual trace.

First public: 2025-09-24.
ArXiv initial submission: 2025-09-24.

[Original paper](https://arxiv.org/abs/2509.20317) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2509.20317/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2509.20317/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2509.20317/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2509.20317/record.json)
[Code](https://github.com/InternLM/SIM-CoT)

## 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.