PC-VOT: Prefix-Coupled Verifier-Induced Optimal Transport for Draft Training

Speculative decoding accelerates LLM inference by using a lightweight draft model to propose candidate tokens that are verified in parallel by the target model. Diffusion drafters further improve drafting efficiency by generating an entire candidate block in a single forward pass. However, a fundamental training-decoding discrepancy remains: conventional objectives train the drafter to match the target distribution pointwise, while decoding depends on the verifier-induced decision regions that determine which draft proposals are accepted. We propose *Prefix-Coupled Verifier-Induced Optimal Transport (PC-VOT)*, a verifier-aligned training framework for diffusion drafters. PC-VOT constructs a verifier-induced feasible set around the Bayes-optimal proposal under stochastic verification and minimizes the point-to-set transport required to reach it. It then couples the transport cost across positions according to prefix reachability and the remaining speculative progress at risk. Theoretical analysis validates that PC-VOT provides a lower bound on expected acceptance length. Extensive experiments across seven math, coding, and dialogue benchmarks show that PC-VOT improves the average decoding speedup by up to 11.2% over DFlash baseline and 6.0% over D-PACE baseline.