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From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons

2026-04-11 13:18:27
Xiangyu Ma, Teng Xiao, Zuchao Li, Lefei Zhang
arXiv_AI
                 
Abstract

Diffusion models promise efficient parallel text generation but rely on bidirectional attention, creating a structural mismatch with pre-trained Autoregressive (AR) models. This incompatibility precludes reusing robust AR priors, necessitating prohibitive pre-training from scratch. To bridge this gap, we propose FLUID, a framework that efficiently adapts AR backbones to the diffusion paradigm. By enforcing Strictly Causal Alignment, FLUID enables seamless initialization from standard GPT-style checkpoints, circumventing the need for massive pre-training. Furthermore, we introduce Elastic Horizons, an entropy-driven mechanism that dynamically modulates denoising strides based on local information density rather than fixed schedules. Experiments demonstrate that FLUID achieves state-of-the-art performance while reducing training costs by orders of magnitude, effectively reconciling established AR foundations with efficient parallel generation. Our code is available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2605.27387

PDF

https://arxiv.org/pdf/2605.27387.pdf

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