Progressive Tree Drafting Doubles LLM Decoding Speed Without Extra Training

Jul 14, 2026

A new preprint introduces Progressive Tree Drafting (PTD), a speculative decoding method that accelerates large language model (LLM) inference by up to 2x. PTD is both training-free and model-agnostic, using a progressive tree structure and stepwise pruning to explore multiple semantic paths in a single forward pass, which improves draft diversity and coherence.

Why it matters: PTD offers a practical way to significantly speed up LLM inference without the need for additional training or auxiliary models.

Full story at: arXiv Computation and Language ↗