Reducing Pretraining-Generation Mismatch in Diffusion Language Models

Abstract:Diffusion language models (dLLMs) generate text through iterative denoising, allowing multiple tokens to be predicted in parallel. However, pretraining may mask tokens throughout a sequence, whereas prompt continuation conditions on an intact prefix. This difference remains in conversion pipelines that denoise entire sequences during the stable stage. We propose Prefix-Conditioned Diffusion (PCD), which samples a boundary, preserves the prefix, and denoises the suffix. The training recipe also applies autoregressive supervision to the prefix. We evaluate PCD in the stable stage of a warmup, stable, and decay conversion pipeline, with inference unchanged. Matched experiments across model families show improvements in reasoning and coding over native diffusion training. A matched continuation study further shows that the advantage persists after a shared decay stage. In a separate reconstruction diagnostic, the full PCD recipe's advantage over native diffusion training reverses as more evaluation prefix tokens are masked. Controlled experiments show lower suffix reconstruction loss with a clean training prefix, an intact evaluation prefix, and no autoregressive loss.

12 pages, 9 figures, 4 tables. Revised manuscript with expanded experiments and analysis

Computation and Language (cs.CL)

arXiv:2608.09424 [cs.CL]

(or arXiv:2608.09424v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.09424 Focus to learn more arXiv-issued DOI via DataCite

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