Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism

• URL: http://arxiv.org/abs/2601.05524v1
• Date: Fri, 09 Jan 2026 04:35:21 GMT
• Title: Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism
• Authors: Yuhao Shen, Tianyu Liu, Junyi Shen, Jinyang Wu, Quan Kong, Li Huan, Cong Wang,
• Abstract summary: We introduce textscDouble (Double Retrieval Speculative Parallelism)<n>We enable the draft model to execute iterative retrieval speculations to break the theoretical speedup limits.<n>Experiments demonstrate state-of-the-art speedup of $textbf5.3times$ on LLaMA3.3-70B and $textbf2.8times$ on Qwen3-32B.
• Score: 19.7914286780195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parallel Speculative Decoding (PSD) accelerates traditional Speculative Decoding (SD) by overlapping draft generation with verification. However, it remains hampered by two fundamental challenges: (1) a theoretical speedup ceiling dictated by the speed ratio between the draft and target models, and (2) high computational waste and pipeline stall due to mid-sequence token rejections of early errors. To address these limitations, we introduce \textsc{Double} (Double Retrieval Speculative Parallelism). By bridging the gap between SD and PSD, our framework resolves the Retrieval \emph{Precision-Efficiency Dilemma} through a novel synchronous mechanism. Specifically, we enable the draft model to execute iterative retrieval speculations to break the theoretical speedup limits; to alleviate rejections without rollback, the target model performs authoritative retrieval to generate multi-token guidance. \textsc{Double} is entirely training-free and lossless. Extensive experiments demonstrate state-of-the-art speedup of $\textbf{5.3}\times$ on LLaMA3.3-70B and $\textbf{2.8}\times$ on Qwen3-32B, significantly outperforming the advanced method EAGLE-3 that requires extensive model training.

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