vLLM adds online-fitted acceptance estimator for Adaptive Verification in draft models

AIvLLM says adaptive verification now works with MTP, EAGLE, DFlash, and DSpark draft checkpoints without a trained confidence head or retraining. The drafter's top-token probability ranks drafts well but overestimates acceptance, so a small logistic fit trained online from the target's accept/reject decisions corrects the calibration. The feature is available on main behind enable_adaptive_verification for draft models whose attention backends support variable-length verification.

AIvLLM says its new online-fitted estimator lets draft models without a confidence head use Adaptive Verification. The estimator uses a logistic regression on the log-odds of the drafter's top token, with coefficients refit from the target model's accept/reject decisions during serving. On DeepSeek-V4-Flash with DSpark, the post reports the estimator outperforms the trained confidence head on AUC and log-loss, and roughly matches it on calibration after excluding the first draft position.