Unlocking Parallelism in Autoregressive Language Models via Speculative Decoding with Progressive Tree Drafting
Speculative decoding has significantly accelerated Large Language Model (LLM) inference by alleviating memory-bound bottlenecks. However, traditional speculative decoding typically relies on auxiliary draft modules, incurring significant training and communication overhead. Although recent methods attempt to generate drafts within the target model itself, they often fail to fully exploit its latent parallel capacity due to a lack of structural coordination. In this paper, we propose \textbf{Progressive Tree Drafting (PTD)}, which employs a structured, guided parallel drafting strategy to harness the model's parallel potential. By coupling a progressive tree structure with a stepwise pruni...
| Authors | Zipeng Gao, Zhi Zheng, Qingrong Xia et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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