Speculative Sampling via Exponential Races

• URL: http://arxiv.org/abs/2504.15475v1
• Date: Mon, 21 Apr 2025 23:02:08 GMT
• Title: Speculative Sampling via Exponential Races
• Authors: Szymon Kobus, Deniz Gündüz,
• Abstract summary: Speculative decoding accelerates large language model inference using a smaller draft model.<n>We propose a novel speculative decoding method via exponential race ERSD that matches state-of-the-art performance.
• Score: 46.8257865686349
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speculative decoding accelerates large language model inference using a smaller draft model. In this paper, we establish a surprising connection between speculative decoding and channel simulation, which aims at simulating a noisy channel using as few bits as possible. This connection allows us to provide an information-theoretic analysis of the speed up that can be achieved by speculative decoding. Leveraging this link, we derive an explicit relation between generation speed-up and the number of tokens $k$ generated by the draft model for large $k$, which serves as an upper bound for all $k$. We also propose a novel speculative decoding method via exponential race ERSD that matches state-of-the-art performance.

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