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[2602.16961] Greedy Multi-Path Block Verification for Faster Decoding in Speculative Sampling
Summary
This paper presents Greedy Multi-Path Block Verification (GBV), a method that enhances the efficiency of speculative decoding in machine learning by improving block verification processes, leading to significant reductions in decoding time and increased throughput.
Why It Matters
As machine learning models become more complex, optimizing decoding processes is crucial for performance. GBV offers a novel approach that not only improves efficiency but also has practical implications for real-time applications in natural language processing and generative AI, making it relevant for researchers and practitioners in these fields.
Key Takeaways
- GBV improves block efficiency by over 30% compared to traditional methods.
- It reduces decoding walltimes by more than 15%, enhancing overall throughput.
- The method is applicable to multi-path verification, extending its utility in complex models.
Computer Science > Information Theory arXiv:2602.16961 (cs) [Submitted on 18 Feb 2026] Title:Greedy Multi-Path Block Verification for Faster Decoding in Speculative Sampling Authors:Rahul Thomas, Arka Pal View a PDF of the paper titled Greedy Multi-Path Block Verification for Faster Decoding in Speculative Sampling, by Rahul Thomas and 1 other authors View PDF HTML (experimental) Abstract:The goal of $L$-step speculative decoding is to accelerate autoregressive decoding of a target model by using a cheaper draft model to generate a candidate path of $L$ tokens. Based on a verification algorithm involving target and draft model probabilities, a prefix of the candidate sequence is accepted, and an additional correction token is sampled from a residual distribution to ensure that the final output adheres to the target distribution. While standard speculative decoding uses a verification algorithm which is independent at each token on the path, a recent extension called block verification uses a joint condition involving all sampled on-path probabilities. Block verification (BV) was shown to be optimal over all verification algorithms which use only on-path probabilities, improving on standard speculative decoding. In this work, we first show that block verification is optimal even over verification algorithms that use off-path probabilities, by constructing an information-agnostic linear program (LP). Further, we can extend our LP to the setting where the draft model samples ...
