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Autori principali: Sun, Haocheng, Wen, Cynthia Xin, Wang, Edward Hong
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2510.03289
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author Sun, Haocheng
Wen, Cynthia Xin
Wang, Edward Hong
author_facet Sun, Haocheng
Wen, Cynthia Xin
Wang, Edward Hong
contents The main advantages of diffusion language models over autoregressive (AR) models lie in their ability to support parallel generation and bidirectional attention, enabling a more controllable generation process. In recent years, open-source mask diffusion language models have emerged, most of which are based on a variant known as absorbing diffusion. However, this paper demonstrates why mask diffusion faces inherent difficulties in achieving parallel generation and bidirectional attention. We also propose the most effective training and inference strategies for mask diffusion.
format Preprint
id arxiv_https___arxiv_org_abs_2510_03289
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Why mask diffusion does not work
Sun, Haocheng
Wen, Cynthia Xin
Wang, Edward Hong
Machine Learning
Artificial Intelligence
Computation and Language
The main advantages of diffusion language models over autoregressive (AR) models lie in their ability to support parallel generation and bidirectional attention, enabling a more controllable generation process. In recent years, open-source mask diffusion language models have emerged, most of which are based on a variant known as absorbing diffusion. However, this paper demonstrates why mask diffusion faces inherent difficulties in achieving parallel generation and bidirectional attention. We also propose the most effective training and inference strategies for mask diffusion.
title Why mask diffusion does not work
topic Machine Learning
Artificial Intelligence
Computation and Language
url https://arxiv.org/abs/2510.03289