# Best-of-Depth: Learning Exploratory Dynamics in Looped Transformers

Authors: Shota Takashiro, Takeshi Kojima, Masanori Koyama, Kohei Hayashi, Yusuke Iwasawa.

Trains a looped transformer using the lowest-loss prediction along its recurrent trajectory. Different depths can produce different valid solutions, which are evaluated on puzzles and code generation.

First public: 2026-11-03.
ArXiv initial submission: 2026-11-03.

[Original paper](https://arxiv.org/abs/2611.04663) · [PDF](https://arxiv.org/pdf/2611.04663) · [HTML](https://arxiv.org/html/2611.04663v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2611.04663/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2611.04663/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2611.04663/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2611.04663/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.

Reading-list version 0.15.2; literature checked through 2026-11-03.