Diffusion Language Model Decoding

Diffusion language model decoding generates text by repeatedly refining masked or noisy token positions, often updating many positions in parallel instead of predicting one token at a time. Common approaches use confidence, attention, token dependencies, or prediction stability to choose what to reveal and when; blockwise schedules, remasking, and learned ordering policies aim to make those choices more reliable. Speculative drafting and verification, search, adaptive length control, and cache or hardware optimizations further target faster inference across reasoning, code, long-form, and multimodal tasks. The central tension is that parallel updates can miss dependencies or commit errors too early, while cautious decoding costs more, so improving speed without sacrificing accuracy, coherence, or diversity remains an open challenge.