RheoSampling: Resolving the One-Hot Dilemma in Stochastic Dynamic-Tree Speculative Decoding
Paper recorded by Signals 4 on 2026-09-18 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21
Category: cs.CL · 自然语言处理 · first seen 2026-09-21
Abstract
Speculative decoding accelerates LLM inference by drafting multiple tokens in parallel, with tree-based methods further improving efficiency through hierarchical structures. Dynamic-tree methods such as EAGLE-3 perform well under greedy decoding via deterministic top-K expansion and global pruning. However, in stochastic decoding (T>0), this mechanism collapses the draft distribution into one-hot
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Cite this page: RheoSampling: Resolving the One-Hot Dilemma in Stochastic Dynamic-Tree Speculative Decoding: the #8 most recent of 200 cs.CL papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/rheosampling-resolving-the-one-hot-dilemma-in-stochastic-dynamic-tree-speculativ.html
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