# Towards Looped Models Done Right, Part II: Rethinking at Fixed Points

Authors: Benhao Huang, Chufan Shi, Junlin Chen, Shicheng Wen, Zhengzhong Liu, Eric Xing, Xuezhe Ma.

Uses near-fixed-point recurrent states to reduce the cost of training, key/value caching, and prefill. Learns how to sample loop depths and changes how the input is reintroduced on each loop.

First public: 2026-10-05.
ArXiv initial submission: 2026-10-05.

[Original paper](https://arxiv.org/abs/2610.06833) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.06833/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.06833/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.06833/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.06833/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.