UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding
Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters have achieved breakthrough performance in frontier production models, but their effectiveness deteriorates as the entropy of target distributions increases due to insufficient draft diversity. To overcome this bottleneck without sacrificing parallelism, we introduce UBTree, a parallel drafter that couples a Unigram proposer with a Bigram selector to construct drafting Trees. The unigram proposer is trained with the standard cross-entropy objective to generate candidate tokens independently for each position, while a lightweight bigram selecto...
| Authors | Chumeng Liang, Linxuan Wang, Xinyu Peng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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