From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons
arXiv:2605.27387v1 Announce Type: cross 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
The proliferation of large language models (LLMs) and the quest for more efficient text generation methods drive innovation in adapting existing architectures.
This breakthrough offers a method to leverage established autoregressive models for diffusion-based text generation, potentially accelerating AI development and reducing computational costs.
Pre-trained GPT-style models can now be efficiently adapted to the diffusion paradigm, sidestepping the need for extensive retraining from scratch for new generative methods.
- · AI researchers
- · Companies developing generative AI
- · Developers of text generation applications
- · Hardware providers for AI inference
- · Organizations focused solely on from-scratch diffusion model training
More diverse and efficient generative AI models become available, leveraging existing robust AR priors.
Reduced computational barriers may democratize access to advanced generative AI capabilities.
The easier integration of different AI paradigms could lead to novel hybrid AI architectures with unforeseen capabilities.
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Read at arXiv cs.AI