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Pruning supplies sufficient budget for retrieval, while retrieval compensates for pruning-induced coverage loss and recovers a","title":"Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding","url":"https://arxiv.org/abs/2605.20104","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.20104v1 Announce Type: cross \nAbstract: Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, recent methods construct expansive draft trees, which unfortunately incur severe VRAM bandwidth and computational overheads that bottleneck end-to-end speedups. While dynamic-depth pruning can reduce this latency by removing marginal branches, it also discards potentially valid candidates, preventing the acceptance rate from reaching the upper bound of dense trees. 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