  Recently, diffusion models have garnered significant interest in the field of
text processing due to their many potential advantages compared to conventional
autoregressive models. In this work, we propose Diffusion-of-Thought (DoT), a
novel approach that integrates diffusion models with Chain-of-Thought, a
well-established technique for improving the reasoning ability of
autoregressive language models. In contrast to autoregressive language models
that make decisions in a left-to-right, token-by-token manner, DoT allows
reasoning steps to diffuse over time through a diffusion language model and
offers greater flexibility in trading-off computation for reasoning
performance. Our experimental results demonstrate the effectiveness of DoT in
multi-digit multiplication, boolean logic, and grade school math problems, with
a small diffusion model outperforming a much larger autoregressive model in
both efficiency and accuracy. In addition to that, DoT showcases promising
self-correction abilities and benefits from existing reasoning-enhancing
techniques like self-consistency decoding. Our findings contribute to the
understanding and development of reasoning with diffusion language models.
