ICML Paper Proposes Continuous-Discrete Diffusion Language Model
zdhnarsil · x · 2026-07-10
Slated for presentation at ICML, the CCDD paper explores a novel architecture for Diffusion Language Models (DLMs). The research indicates that while continuous diffusion models offer greater expressivity and can perform latent reasoning akin to recurrent Transformers, discrete models hold an edge in trainability. By systematically integrating continuous and discrete diffusion, the model balances expressivity and trainability, marking the first connection between DLMs and recurrent Transformers while underscoring the critical roles of latent reasoning and representation learning.
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