From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons
Quick Answer
This paper shows that The FLUID framework adapts autoregressive models to diffusion paradigms, enabling seamless initialization from GPT-style checkpoints and significantly reducing training costs.
Quick Take
By employing Strictly Causal Alignment and Elastic Horizons, FLUID achieves state-of-the-art performance while reconciling traditional AR foundations with efficient parallel text generation.
Key Points
- FLUID enables efficient adaptation of autoregressive models to diffusion frameworks.
- Strictly Causal Alignment allows initialization from existing GPT-style checkpoints.
- Elastic Horizons dynamically adjusts denoising strides based on local information density.
- FLUID reduces training costs by orders of magnitude compared to traditional methods.
- Experiments show FLUID achieves state-of-the-art performance in text generation.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2605. 27387v1 Announce Type: new 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 https://github. com/Oli-lab-nun/FLUID/tree/main.
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