# From Growing to Looping: A Unified View of Iterative Computation in LLMs

Authors: Ferdinand Kapl, Emmanouil Angelis, Kaitlin Maile, Johannes von Oswald, Stefan Bauer.

Compares depth growing and weight-tied loops using matched model budgets and interventions on intermediate layers. Finds related patterns of repeated computation and improves reasoning by adding middle-block loops to depth-grown models during inference or continued training.

First public: 2026-02-18.
ArXiv initial submission: 2026-02-18.

[Original paper](https://arxiv.org/abs/2602.16490) · [PDF](https://arxiv.org/pdf/2602.16490) · [HTML](https://arxiv.org/html/2602.16490v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2602.16490/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2602.16490/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2602.16490/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2602.16490/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.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

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