Test-Time Speculation
Speculative decoding accelerates LLM inference by using a fast draft model to generate tokens and a more accurate target model to verify them. Its performance depends on the $\textit{acceptance length}$, or number of draft tokens accepted by the target. Our studies show that the acceptance length of even state-of-the-art speculators, like DFlash, EAGLE-3 and PARD degrade with generation length, reaching values close to 1 (i.e. no speedup) within just a few thousand output tokens, making speculators ineffective for long-response tasks. Acceptance lengths decline because most speculators are trained offline on short sequences, but are forced to match the target model on much longer outputs ...
| Authors | Avinash Kumar, Sujay Sanghavi, Poulami Das |
|---|---|
| Primary category | cs.CL |
| Published | 10 May 2026 |
| Last updated at source | 19 May 2026 |
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