Margins, Not Windows: Training-Free Per-Step Lossy Speculative Decoding
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 th...
| Authors | Oszkár Urbán, Young D. Kwon, Stylianos I. Venieris et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 July 2026 |
| Last updated at source | 28 September 2026 |
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