# LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models

Authors: Byeongho Yu, Junhyuk So, Eunhyeok Park.

Uses differences between early- and late-loop predictions to adjust token sampling at inference time, without retraining the model.

First public: 2026-09-21.
ArXiv initial submission: 2026-09-21.

[Original paper](https://arxiv.org/abs/2609.24196) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.24196/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.24196/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.24196/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.24196/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.