Think while You Generate: Discrete Diffusion with Planned Denoising
Sulin Liu Juno Nam Andrew Campbell Hannes Stark Yilun Xu Tommi Jaakkola Rafael Gomez-Bombarelli
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce *Discrete Diffusion with Planned Denoising* (DDPD), a novel framework that separates the generation process into two models: a planner and a denoiser. At inference time, the planner selects which positions to denoise next by identifying the most corrupted positions in need of denoising, including both initially corrupted and those requiring additional refinement. This plan-and-denoise approach enables more efficient reconstruction during generation by iteratively identifying and denoising corruptions in the optimal order. DDPD outperforms traditional denoiser-only mask diffusion methods, achieving superior results on language modeling benchmarks such as *text8*, *OpenWebText*, and token-based generation on *ImageNet 256 × 256*. Notably, in language modeling, DDPD significantly reduces the performance gap between diffusion-based and autoregressive methods in terms of generative perplexity.  Code is available at [github.com/liusulin/DDPD](https://github.com/liusulin/DDPD).
discrete diffusion, generative models
DDPD separates the discrete diffusion generation process into two models: a planner that identifies which positions are corrupted and should be denoised next, and a denoiser that corrects them.