dLLM
A class of language models that apply diffusion model principles to discrete text sequence generation. These models generate text through masked-denoising iterations rather than autoregressive decoding, enabling parallel generation and local infilling operations.
Timeline (last 90 days)
Plan-and-Patch is a plan-and-act framework whose core is using a diffusion language model (dLLM) to generate and repair plans.
Since all positions can in principle be processed in parallel simultaneously, dLLMs are often faster than autoregressive models when generating fixed-length sequences.
All Facts (2)
Plan-and-Patch is a plan-and-act framework whose core is using a diffusion language model (dLLM) to generate and repair plans.
50%UnverifiedSince all positions can in principle be processed in parallel simultaneously, dLLMs are often faster than autoregressive models when generating fixed-length sequences.
50%