# Looped Uniform Diffusion Language Models

Authors: Saiteja Utpala, Chieh-Hsin Lai, Yuki Mitsufuji, Praneeth Vepakomma.

Repeats a shared block inside each denoising step of a uniform diffusion language model. Studies how training-time loop counts and adaptive stopping affect language modeling quality, training cost and downstream accuracy.

First public: 2026-10-21.
ArXiv initial submission: 2026-10-21.

[Original paper](https://arxiv.org/abs/2610.30066) · [PDF](https://arxiv.org/pdf/2610.30066) · [HTML](https://arxiv.org/html/2610.30066v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.30066/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.30066/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.30066/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.30066/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.
- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. Some produce written chains of thought.

Reading-list version 0.11.0; literature checked through 2026-10-26.