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Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate individually plausible but mutually inconsistent tokens. Recent work shows that \emph{soft tokens} can mitigate this issue by representing uncertain positions with conti...
It is shown decoder-only MDMs, despite a larger modeling space, can achieve significant inference speedups and comparable perplexity with techniques like temperature annealing, offering a path to reduced inference compute.
Overall, same-model draft-and-refine provides evidence that bidirectional refinement is a useful decoding primitive for DLMs, while speculative correction demonstrates a training-free route to fast DLM generation.
This work systematically investigates the impact of the projection unit on LoRP methods, and extends existing LoRP approaches by introducing an additional degree of freedom, projection granularity, beyond the traditional rank hyperparameter, which enables a framework capable of performing fine-grained projections, whic...
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