# Adaptive-Depth Is a Finishing Stage

Authors: Andrei Piterbarg, Sean McLeish, Dhruv Pai, Benjamin Keigwin.

Trains a looped language model at a fixed depth first, then introduces varying depths only near the end of training. Compares the resulting training cost and performance across inference depths with varying depth throughout training.

First public: 2026-10-09.
ArXiv initial submission: 2026-10-09.

[Original paper](https://arxiv.org/abs/2610.14252) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.14252/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.14252/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.14252/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.14252/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.