# Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts

Authors: Di He, Pengxiang Li, Da Chang, Qingyan Meng, Lu Yin, Shiwei Liu.

Studies how to train mixture-of-experts transformers with more than two recurrent passes. Combines stabilized residual updates, reintroduced input information, and loop-specific routing to support deeper reuse of the shared model.

First public: 2026-10-01.
ArXiv initial submission: 2026-10-01.

[Original paper](https://arxiv.org/abs/2610.01153) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.01153/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.01153/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.01153/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.01153/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.