# Transformers Provably Learn to Internalize Chain-of-Thought

Authors: Yixiao Huang, Hanlin Zhu, Zixuan Wang, Jiantao Jiao, Stuart Russell, Somayeh Sojoudi, Song Mei.

Provides theoretical and empirical results on when transformers trained with a curriculum learn to internalize explicit reasoning steps.

First public: 2026-05-27.
ArXiv initial submission: 2026-05-27.

[Original paper](https://arxiv.org/abs/2605.28600) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2605.28600/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2605.28600/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2605.28600/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2605.28600/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.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

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.