Polybasic Speculative Decoding Through a Theoretical Perspective
Ruilin Wang, Huixia Li, Yuexiao Ma, Xiawu Zheng, Fei Chao, Xuefeng Xiao, Rongrong Ji

TL;DR
This paper introduces a theoretically grounded polybasic speculative decoding framework that significantly accelerates large language model inference while maintaining output quality.
Contribution
It presents a novel multi-model speculative decoding approach with rigorous theoretical analysis and practical implementation, surpassing traditional dualistic methods.
Findings
Achieves up to 4.43x speedup on various LLMs
Provides theoretical characterization of optimal inference time
Supports integration with existing speculative techniques
Abstract
Inference latency stands as a critical bottleneck in the large-scale deployment of Large Language Models (LLMs). Speculative decoding methods have recently shown promise in accelerating inference without compromising the output distribution. However, existing work typically relies on a dualistic draft-verify framework and lacks rigorous theoretical grounding. In this paper, we introduce a novel \emph{polybasic} speculative decoding framework, underpinned by a comprehensive theoretical analysis. Specifically, we prove a fundamental theorem that characterizes the optimal inference time for multi-model speculative decoding systems, shedding light on how to extend beyond the dualistic approach to a more general polybasic paradigm. Through our theoretical investigation of multi-model token generation, we expose and optimize the interplay between model capabilities, acceptance lengths, and…
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