The article discusses the concept of using discrete language diffusion models for text generation, specifically highlighting how BERT's masked language modeling can be generalized into a diffusion framework. It explores the evolution from traditional models like BERT and GPT to the newer Gemini Diffusion model, and introduces the idea of transforming BERT's training objective into a generative process through variable masking rates. The author also notes the existence of related work, such as DiffusionBERT, which performs similar tasks with rigorous testing.

The article introduces Fast-dLLM, a method for accelerating diffusion-based large language models (LLMs) by implementing a block-wise approximate Key-Value (KV) Cache and a confidence-aware parallel decoding strategy. This approach addresses the slow inference speed of diffusion LLMs and mitigates quality degradation during parallel token decoding, achieving significant throughput improvements while maintaining accuracy. Experimental results show up to 27.6 times higher throughput, facilitating the practical deployment of diffusion LLMs.