# Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces

Authors: DiJia Su, Sainbayar Sukhbaatar, Michael Rabbat, Yuandong Tian, Qinqing Zheng.

Randomizes which reasoning steps remain visible during training so one model can interpolate between direct and explicit reasoning.

First public: 2024-10-13.
ArXiv initial submission: 2024-10-13.

[Original paper](https://arxiv.org/abs/2410.09918) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2410.09918/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2410.09918/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2410.09918/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2410.09918/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.

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.