When, What, and How: Rethinking Retrieval-Enhanced Speculative Decoding

Speculative decoding (SD) has emerged as an effective technique to accelerate large language model (LLM) inference without compromising output quality. However, the achievable speedup largely depends on the effectiveness of the drafting model. While model-based methods like EAGLE-2 are accurate but costly, retrieval-enhanced methods like SAM-Decoding rely on heuristic switching strategies that often trigger unnecessary retrievals. To address this, we propose ReSpec ($\mathbf{\text{Re}}$trieval-enhanced $\mathbf{\text{Spe}}$culative Decoding), a novel framework that transforms heuristic drafter switching into adaptive decision-making. ReSpec features three core innovations: 1) An $\mathbf{\text{entropy-guided}}\mathbf{\text{adaptive}}\mathbf{\text{trigger}}$ quantifies contextual predictability to initiate retrieval only when uncertainty is low, avoiding costly low-quality speculations. 2) A $\mathbf{\text{feedback-driven}}\mathbf{\text{candidate}}\mathbf{\text{selection}}$ leverages historical feedback to organize multiple high-quality candidates for parallel verification, maximizing retrieval utility. 3) A source-aware $\mathbf{\text{relaxed}}\mathbf{\text{verification}}\mathbf{\text{strategy}}$ applies strict checks to model-generated drafts while using a relaxed verification for retrieved drafts, achieving a better balance between accuracy and efficiency. Extensive experiments on Spec-Bench demonstrate that ReSpec achieves state-of-the-art acceleration,outperforming EAGLE-2 and SAM-Decoding by over $33\mathrm{\%}$ and $25\mathrm{\%}$, respectively, while maintaining output quality.