Think While You Generate: Discrete Diffusion with Planned Denoising
Authors: name: Sulin Liu, name: Juno Nam, name: Andrew Campbell, name: Hannes Stärk, name: Yilun Xu, name: Tommi Jaakkola, name: Rafael Gómez-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 times 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 https://github.com/liusulin/DDPD.