Overview of Diffusion Language Model (Diffusion LLM) Research Trends

The existing Autoregressive (AR) approach's sequential generation has limitations: increased latency when generating long sentences, low GPU compute resource utilization, and difficulty in flexibly modifying sentence structure.

Diffusion Language Models (Diffusion LLM, dLLM) start from a state where the entire answer region is masked (Masking), and fill in tokens in parallel through a multi-step Denoising process. This applies the principles of image generation models to discrete text tokens.

Key research and models include the following:

• LLaDA: Adopts a Masked Diffusion approach, applying a noise schedule based on the information content of the text
• DiffusionGemma: A diffusion model based on Google's Gemma architecture
• Other notable models: Mercury, Dream 7B, Nemotron Diffusion, etc.

These dLLMs can predict multiple tokens simultaneously in a single Forward pass, giving them the potential to efficiently utilize compute resources and reduce generation latency.