JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting
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. This ceiling has been difficult to break because prior head-based SD methods face a causality-efficiency dilemma. Autoregressive drafters produce path-conditioned candidates that are effective for tree speculative decoding with higher acceptance length, but their drafting cost grows with tree depth. Bidirectional block-diffusion drafters generate all positions in one pass, but their branch-agnostic marginal...
| Authors | Lanxiang Hu, Zhaoxiang Feng, Yulun Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 June 2026 |
| Last updated at source | 3 October 2026 |
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