Adaptive Verification Without a Confidence Head

Confidence scoring is now supported for all draft-model-based speculator types. In the absence of a confidence head, an online-fitted estimator derives a feature from the drafter's logits to predict the target model's probability of accepting it, which is used directly as a confidence score for Adaptive Verification. The estimator's coefficients are continuously fitted, adapting to the incoming data traffic. This feature landed in PR #52228, and is enabled with enable_adaptive_verification for draft models without a confidence head. vLLM's Adaptive Verification (AV) enables verification of a subset of the drafted tokens during speculative decoding. Draft tokens with low probability of acceptance are trimmed from the batch before running the target model's forward pass, saving on compute. This optimization is particularly beneficial while serving large batch sizes, because in that regime the GPU becomes compute-bound. See this vLLM blog post for more details. AV needs a per-token estimate of acceptance probability, which is why, until now, it only supported DSpark drafters. DSpark models often ship with a confidence head: a small linear layer over the drafter's hidden state for each position, trained as a binary classifier for "will the target accept this token?" Draft models that lack a confidence head, such as…