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arXiv · 2502.19732

Speculative Decoding and Beyond: An In-Depth Survey of Techniques

Yunhai Hu·Zining Liu·Zhenyuan Dong·Tianfan Peng·Bradley McDanel·Sai Qian Zhang
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Abstract

Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models, particularly for real-time applications. While traditional optimization approaches like pruning and quantization often compromise model quality, recent advances in generation-refinement frameworks demonstrate that this trade-off can be significantly mitigated. This survey presents a comprehensive taxonomy of generation-refinement frameworks, analyzing methods across autoregressive sequence tasks. We categorize methods based on their generation strategies (from simple n-gram prediction to sophisticated draft models) and refinement mechanisms (including single-pass verification and iterative approaches). Through systematic analysis of both algorithmic innovations and system-level implementations, we examine deployment strategies across computing environments and explore applications spanning text, images, and speech generation. This systematic examination of both theoretical frameworks and practical implementations provides a foundation for future research in efficient autoregressive decoding.

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Yunhai Hu, Zining Liu, Zhenyuan Dong, Tianfan Peng, Bradley McDanel, Sai Qian Zhang. 2025-02-27. Speculative Decoding and Beyond: An In-Depth Survey of Techniques. https://arxiv.org/abs/2502.19732

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