[Submitted on 3 Jul 2026]
Title:Margins, Not Windows: Training-Free Per-Step Lossy Speculative Decoding
View a PDF of the paper titled Margins, Not Windows: Training-Free Per-Step Lossy Speculative Decoding, by Oszk\'ar Urb\'an and 3 other authors
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Abstract:Speculative decoding accelerates LLM inference by drafting candidate tokens and verifying them in parallel. Tree-attention drafters such as EAGLE-3 are widely adopted, yet typically hold two decisions fixed: (1) a strict token-match verification rule and (2) a static draft-tree shape. Prior work relaxes each in isolation under limiting assumptions: long draft chains for training-free lossy verification, and adaptive tree shaping under a fixed token budget. We introduce AdaptiveSpec, a training-free per-step speculative decoding method that adapts both decisions from internal signals already produced during decoding. A per-step margin rule promotes a mismatched draft-proposed token when the ratio of the target's probability on the drafted token to its top-1 probability exceeds a threshold with no dependence on draft length or underlying drafter architecture. A per-step tree policy adjusts the draft tree's depth, width, and node count directly from a fused signal of draft top-1 confidence and a rolling acceptance history capturing recent draft-target agreement, allowing the total draft count to vary rather than only be redistributed. The two adaptations operate on orthogonal axes and compound in effect. Implemented on the SGLang production-grade serving engine, AdaptiveSpec improves throughput over the state-of-the-art autoregressive speculative decoding method EAGLE-3 by up to 56%, recovering 93% to fully lossless task accuracy across GSM8K, MATH-500, and HumanEval on three target models (DeepSeek-R1-Distill-Llama-8B, Llama-3.1-8B-Instruct, Qwen3-8B).
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.02897 [cs.CL]
(or arXiv:2609.02897v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.02897
arXiv-issued DOI via DataCite
Submission history
From: Oszkár Urbán [view email] [v1] Fri, 3 Jul 2026 20:43:49 UTC (378 KB)
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