Benefits of text diffusion sampling beyond the code domain

Determine whether text diffusion sampling can provide greater benefits in application domains beyond code, specifically including mathematical reasoning and general-purpose text tasks, by conducting further model iterations and comprehensive empirical evaluations to assess capability improvements in these broader settings.

Background

The paper introduces Stable-DiffCoder, a diffusion-based LLM for code that, under controlled conditions, outperforms a comparable autoregressive baseline using the same architecture and data. The authors argue that diffusion-style training can act as principled data augmentation and improve code modeling quality.

However, the work is primarily confined to the code domain and does not include large-scale training or evaluation on other areas such as mathematical reasoning or general-purpose text. The authors therefore explicitly flag uncertainty about whether the advantages observed for code would extend—potentially even more strongly—to broader domains, motivating future model iterations and empirical studies.