Skip to main content

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

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: · Added to this list: October 18, 2026

Read the paper ↗

Cite this paper

ArXiv preprint

arXiv:2610.06833 · Initial submission: 2026-10-05

BibTeX · CSL-JSON

A published-version citation has not been verified for this entry. This does not mean the paper is unpublished.

Choose the version you used. The arXiv link may serve a revision; check the original paper for the exact result, assumptions and limitations you cite.

Explore this approach

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.

Read or reuse this entry

Plain-text Markdown · JSON record · Find in the full list

Our summary and categories are a reading aid, not the authors’ abstract. Use the original paper for research claims. Suggest a correction.