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The paper targets a real limitation of masked diffusion language models: they struggle to model dependencies between tokens they decode in parallel, which forces a trade-off between output quality and speed. Its approach moves generation into a learned continuous latent space and then distills the prior into a few-step model. The reported speedups and quality gains are self-reported, so readers should check them against the paper's benchmarks and compare them with autoregressive baselines before drawing conclusions.
DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling
DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling Diffusion language models intrinsically fail to capture correlations between decoded tokens, which leads to a harsh trade-off between sampling quality and throughput. To solve this issue, we propose DiLaDiff, a variant of masked diffusion language models with three components: (1) a continuous latent space with semantic capabilities, learned by an auto-encoder fine-tuned from an existing masked diffusion language model; (2) a latent diffusion model learning the prior over the encoder distribution; (3) a consistency model distilling the learned prior into a few-step latent generative model. We show that, even without distillation, our latent-guided diffusion model outperforms the masked diffusion baseline while significantly accelerating inference. Consistency distillation further lowers the computational overhead of continuous diffusion, such that the latent is generated in negligible time compared to discrete decoding. kkreis Sat, 10/31/2026 - 20:14
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