Revisiting Lossy Verification in Speculative Decoding: Mechanisms and Failure Modes
Tianyu Wang · hf · 2026-07-31
Speculative decoding accelerates LLM inference by using a lightweight draft model to propose tokens for parallel verification by a larger model. Recent lossy verification schemes improve efficiency by relaxing strict distributional matching, but this silently rewrites the decoding distribution, leading to unstable or degraded generation quality.
This paper provides a systematic analysis of lossy verification, classifying existing approaches into two categories:
- Truncation-based Verification: The study identifies a fundamental pitfall where performance can degrade significantly compared to true truncation sampling baselines due to distributional distortion.
- Collaborative Verification: It reveals that controlling the overshoot of draft probabilities relative to target probabilities is crucial to preventing low-quality outputs.
The authors also construct a diagnostic evaluation framework and open-source their code.
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