Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding
Tree-based speculative decoding accelerates autoregressive generation by verifying multiple draft candidates in parallel, but this advantage weakens for sparse Mixture-of-Experts (MoE) models. As the draft tree grows, different branches activate different experts, expanding the union of activated experts and substantially increasing target-side verification cost. We propose EVICT, a training-free, hyperparameter-free, and lossless adaptive verification method for MoE speculative decoding. EVICT makes every verified token count by truncating the draft tree before target verification and retaining only the cost-effective prefix. It leverages fine-grained drafter signals to estimate candidat...
| Authors | Lehan Pan, Ziyang Tao, Xiao Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 May 2026 |
| Last updated at source | 5 September 2026 |
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