A Ticket from Marginals to Joints: Coupled-Noise Distillation for One-Step Block Generation in Diffusion Language Models

Abstract

Diffusion language models (dLLMs) predict all tokens of a block in parallel, but a single forward pass samples each position from its own marginal distribution, so the tokens need not form a coherent block. We ask whether a discrete masked model can commit an entire block in one pass when its mask embeddings are perturbed by a sampled Gaussian noise field: the same noise should give the same coherent continuation, and different noise should give different ones. We propose CONDOR (Coupled-Noise Distillation for One-Step Readout), which trains such a model from scratch without a target-side encoder or an autoregressive teacher. Training combines two signals. On real text, the model predicts masked tokens under several noise samples and is supervised only through the sample that fits the ground truth best, so different noise can specialize to different continuations. For the remaining samples, the model refines its own one-pass prediction over several decoding steps under the same fixed noise and then distills that refined block back into a single pass. On a controlled TinyStories setting, this recipe yields coherent one-pass continuations that vary with the noise, both for a single block and, with a block-causal variant, when blocks are generated one after another.