Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding

• URL: http://arxiv.org/abs/2504.00030v2
• Date: Thu, 03 Apr 2025 12:31:40 GMT
• Title: Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding
• Authors: Aayush Gautam, Susav Shrestha, Narasimha Reddy,
• Abstract summary: Speculative decoding accelerates large language model inference.<n>We introduce textitGammaTune and textitGammaTune+, training-free adaptive algorithms that dynamically adjust speculation length based on token acceptance rates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speculative decoding accelerates large language model (LLM) inference by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, selecting an optimal speculation length is critical for maximizing speedup while minimizing wasted computation. We introduce \textit{GammaTune} and \textit{GammaTune+}, training-free adaptive algorithms that dynamically adjust speculation length based on token acceptance rates using a heuristic-based switching mechanism. Evaluated on SpecBench across multiple tasks and model pairs, our method outperforms other heuristic-based approaches and fixed-length speculative decoding, achieving an average speedup of 15\% ($\pm$5\%) with \textit{GammaTune} and 16\% ($\pm$3\%) with \textit{GammaTune+}, while reducing performance variance. This makes \textit{GammaTune} a robust and efficient solution for real-world deployment.

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