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

FLUID is a new framework designed to efficiently adapt Autoregressive (AR) backbones to the diffusion paradigm for parallel text generation. It enables initialization from GPT-style models and introduces an entropy-driven mechanism called Elastic Horizons, achieving state-of-the-art performance with significantly reduced training costs.