Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation. In this work, we introduce a class of block diffusion language models that interpolate between discrete denoising diffusion and autoregressive models. Block diffusion overcomes key limitations of both approaches by supporting flexible-length generation and improving inference efficiency with KV caching and parallel token sampling. We propose a recipe for building effective block diffusion models that includes an efficient training algorithm, estimators of gradient variance, and data-driven noise schedules to minimize the variance. Block diffusion sets a new state-of-the-art performance among diffusion models on language modeling benchmarks and enables generation of arbitrary-length sequences. We provide the code, along with the model weights and blog post on the project page: https://m-arriola.com/bd3lms/

本研究针对扩散语言模型在似然建模和固定长度生成方面的局限性，提出了一类块扩散语言模型，能够实现灵活长度的生成并提高推理效率。研究表明，块扩散模型在语言建模基准测试中设置了新的最先进性能，并支持生成任意长度的序列。

块扩散：在自回归和扩散语言模型之间插值