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Research questionHow can lossy speculative decoding accelerate LLM inference without distorting token distributions or degrading generation quality?Speculative decoding uses a lightweight draft model to propose tokens that a larger target model verifies in parallel. Relaxing that verification can improve efficiency, but it may silently change the sampling distribution and produce unstable or lower-quality generations.
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Latest papersRecent research connected to this question, newest first.Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure ModesThe analysis covers truncation-based and collaborative verification schemes and uses curated benchmark diagnostics. It reports distributional degradation for truncation-based methods relative to true truncation sampling, and examines overshoot suppression, supervision quality, and draft–target interpolation in collaborative verification.research paper · Sep 4, 2026
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