REDDIT · REDDIT// 1d agoRESEARCH PAPER

NUS researchers drop DMax self-refining dLLM

DMax addresses error accumulation in parallel decoding for diffusion language models by reformulating decoding as a progressive self-refinement process. The framework achieves significant speedups—averaging 1,338 tokens per second—while maintaining performance on math and coding benchmarks.

// ANALYSIS

This is a breakthrough for non-autoregressive generation, proving that parallel filling can be both faster and more accurate than sequential generation if the model is trained to handle its own uncertainty. Soft Parallel Decoding avoids binary commitments, preserving flexibility until the final generation step.

// TAGS

diffusion-modelsdllmsparallel-decodingllmnusinference-optimizationopen-sourcedmax

DISCOVERED

1d ago

2026-04-10

PUBLISHED

1d ago

2026-04-10

RELEVANCE

9/ 10

AUTHOR

44th--Hokage