HC-DLM bridges discrete and continuous diffusion by coupling token generation with a shared latent trajectory, enabling better reasoning and constraint satisfaction than purely discrete or continuous approaches.
This paper proposes Hierarchical Continuous Diffusion Language Models (HC-DLM), which combines discrete token generation with continuous latent states in a single denoising process. Unlike existing approaches that treat these separately, HC-DLM uses the continuous latent as the only persistent state, reading tokens from it at each step.