# Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence

Authors: Sean McLeish, Ang Li, John Kirchenbauer, Dayal Singh Kalra, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Jonas Geiping, Tom Goldstein, Micah Goldblum.

Adds recurrent depth to pretrained language models and trains them to use repeated internal computation without pretraining from scratch.

First public: 2025-11-10.
ArXiv initial submission: 2025-11-10.

[Original paper](https://arxiv.org/abs/2511.07384) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2511.07384/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2511.07384/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2511.07384/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2511.07384/record.json)
[Code](https://github.com/mcleish7/retrofitting-recurrence)

## 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.