Diffusion language models can achieve similar transparency to autoregressive models by treating denoised token states as interpretable checkpoints, but their ability to change all tokens simultaneously enables novel reasoning patterns that are harder to understand.
This paper investigates whether diffusion-based language models are less interpretable than traditional autoregressive models. By identifying interpretable token bottlenecks between denoising steps, the authors show DiffusionGemma's reasoning can be made nearly as transparent as standard models, though diffusion's parallel token updates create unique interpretability challenges.