CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think
Published in -, 2026
Revealed the theoretical suboptimality of pointwise token rounding in continuous DLMs. Proposed a contextual autoregressive decoder to replace linear rounding, enabling sequence-aware discretization. Demonstrated that continuous diffusion models can rival discrete DLMs when rounding is properly modeled.
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