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arXiv · 2503.09573

Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

Marianne Arriola·Aaron Gokaslan·Justin T. Chiu·Zhihan Yang·Zhixuan Qi·Jiaqi Han·Subham Sekhar Sahoo·Volodymyr Kuleshov
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Abstract

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

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Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov. 2025-03-12. Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models. https://arxiv.org/abs/2503.09573

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