A Practical Investigation of Training-free Relaxed Speculative Decoding

Guoxuan Xia; Luka Ribar; Paul Balanca

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arXiv · 2607.08690

A Practical Investigation of Training-free Relaxed Speculative Decoding

Guoxuan Xia·Luka Ribar·Paul Balanca

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Abstract

Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in parallel by the LLM. Standard speculative decoding is lossless: its rejection and resampling steps exactly preserve the LLM's sampling distribution. Recent work argues that relaxing this strict guarantee can yield further speed-ups, controlled capability-speed trade-offs, or even capability gains. We practically investigate training-free relaxed speculative decoding techniques, unify existing approaches within a shared framework, benchmark them on contemporary settings, and distil takeaways and empirical findings for practitioners. Important takeaways include: relaxation can require considerable capability evaluation unlike lossless speculative decoding, and many relaxed approaches rely on a drafter that is a good language model, making them unsuited for lightweight dedicated multi-token-prediction drafters.

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Guoxuan Xia, Luka Ribar, Paul Balanca. 2026-07-09. A Practical Investigation of Training-free Relaxed Speculative Decoding. https://arxiv.org/abs/2607.08690

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