New papers advance speculative decoding for LLM inference

Five new arXiv papers propose techniques to accelerate LLM inference via speculative decoding, covering unified kernels (SonicSampler), linear-attention adaptation (SpecLA), vocabulary-based drafting, adaptive verification depth, and a negative result for PEFT-based drafting. These methods aim to improve draft quality and verification efficiency while maintaining output quality.