# Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models

Authors: Dian Jin, Kairong Han, Baohong Li, Xinpeng Dong, Zijing Hu, Nuanqiao Shan, Fei Wu, Kun Kuang.

Extracts reasoning-guiding token chains and prioritizes them during diffusion-model training to improve denoising speed and accuracy. This is a related text-generating approach, not reasoning without a written trace.

First public: 2026-09-23.
ArXiv initial submission: 2026-09-23.

[Original paper](https://arxiv.org/abs/2609.28272) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.28272/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.28272/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.28272/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.28272/record.json)

## Approaches

- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.

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.