# Closing the Loop: Practical Training Recipes for Looped Language Models

Authors: Andrei Marchenko, Viacheslav Bezrukov, Oleg Kashurin, Inessa Fedorova, Dmitry Bocharov, Yuliana Shakhvalieva, Maria Tikhonova, Valerii Ternovskii.

Gives training recipes for looped language models both from scratch and by converting pretrained models. Uses controlled data and compute comparisons to separate the benefit of recurrence from the training setup.

First public: 2026-09-30.
ArXiv initial submission: 2026-09-30.

[Original paper](https://arxiv.org/abs/2610.00673) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.00673/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.00673/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.00673/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.00673/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.