Skip to main content

Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models

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: · Added to this list: October 18, 2026

Cite this paper

ArXiv preprint

arXiv:2609.28272 · Initial submission: 2026-09-23

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 This does not mean the paper is unpublished.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

Alternative generation methods — 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.

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.