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JetFlow: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting

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arXiv:2606. 18394v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it faces a scaling limitation: increasing the draft budget improves speed only when acceptance remains high and drafting overhead stays low.

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Authors: Lanxiang Hu, Zhaoxiang Feng, Yulun Wu, Haoran Yuan, Yujie Zhao, Yu-Yang Qian, Bojun Wang, Daxin Jiang, Yibo Zhu, Tajana Rosing, Hao Zhang
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because JetFlow: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, under, prefix, token, line, rate, length. Source: arxiv cs.CL (NLP).
arXiv: 2606.18394

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