[Paper] WaveFront Decoding: Parallelized Self-Speculative Decoding for Looped Language Models

Wavefront Decoding (WFD) is a training-free self-speculative decoding framework for looped language models, which typically suffer from high decoding latency due to sequential recurrent-block calls. WFD addresses this by concurrently batching drafting and verification within the same recurrent calls, leveraging intermediate recurrence outputs for draft predictions and weight sharing for processing mixed-depth states. This method organizes states into a diagonal wavefront, continuously drafting new positions while advancing earlier ones. WFD achieves significant speedups, such as 2.42x on Ouro-2.6B and 3.54x on Huginn-3.5B, outperforming draft-then-verify methods.