Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language
  Models
Jiacheng Ye Shansan Gong Liheng Chen Lin Zheng Jiahui Gao Han Shi Chuan Wu Zhenguo Li Wei Bi Lingpeng Kong
Diffusion models have gained attention in text processing, offering many
potential advantages over traditional autoregressive models. This work explores
the integration of diffusion models and Chain-of-Thought (CoT), a
well-established technique to improve the reasoning ability in autoregressive
language models. We propose Diffusion-of-Thought (DoT), allowing reasoning
steps to diffuse over time through the diffusion process. In contrast to
traditional autoregressive language models that make decisions in a
left-to-right, token-by-token manner, DoT offers more flexibility in the
trade-off between computation and reasoning performance. Our experimental
results demonstrate the effectiveness of DoT in multi-digit multiplication and
grade school math problems. Additionally, 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 capabilities in diffusion language
models.