Discrete Diffusion Enables Lossless Speedups in Large Language Models

Discrete diffusion methods allow for lossless acceleration of LLM inference by transforming the autoregressive generation process. This approach aims to reduce latency without the typical perplexity degradation associated with standard quantization or pruning techniques.