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AI CLUSTERED EVENT · 10/14/2026

vLLM Adds Online-Fitted Estimator for Adaptive Verification in Speculative Decoding

1 reports archived1 independent sourcesupdated 10/14/2026, 08:00:00
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vLLM introduced an online-fitted estimator based on drafter logits to enable Adaptive Verification for draft models lacking explicit confidence heads. This mechanism dynamically trims low-probability tokens by predicting acceptance rates in real-time, reducing compute overhead in large-batch serving while maintaining lossless guarantees.

LATEST/vLLM introduced an online-fitted estimator based on drafter logits to enable Adaptive Verification for draft models lacking explicit confidence heads. This mechanism dynamically trims low-probability tokens by predicting acceptance rates in real-time, reducing compute overhead in large-batch serving while maintaining lossless guarantees.

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Total 1 reports · Latest first
  1. vLLM 官方博客T1·68 pts
    • vLLM added an online-fitted estimator that derives features from drafter logits to predict acceptance probability for models without confidence heads.
    • Adaptive Verification reduces target model forward pass computation by trimming low-probability tokens early, benefiting high-concurrency scenarios.
    • The feature is merged via PR #52228 and can be enabled using the `enable_adaptive_verification` configuration flag.
vLLM Adds Online-Fitted Estimator for Adaptive Verification in Speculative Decoding | Today for AI