Understanding Target Probability Reuse in Speculative Decoding
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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arXiv:2606. 03819v1 Announce Type: new Abstract: One-shot block drafters for speculative decoding generate the full draft in a single forward pass, achieving strong throughput by eliminating sequential token generation.
arXiv:2608.20375v1 Announce Type: new Abstract: Tree-based speculative decoding raises the mean accepted tokens of standard speculative decoding by verifying multiple draft paths, and existing tree b...
TreeGraft introduces a multi-drafter framework that combines drafters of varying costs to build a shared draft tree for tree-based speculative decoding. The stronger drafter rescues and rescoring candidates from the weaker drafter, while a lightweight scheduler decides when to invoke the stronger drafter to manage cost. Experiments on 10 model pairs and 6 benchmarks show TreeGraft improves over the best single-drafter strategy by an average of 15.1% and up to 26.6%.
Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency by predicting an entire token block in parallel, but their position-wise distributions are marginal rather than conditioned on tokens selected along each draft path.
arXiv:2606. 00487v1 Announce Type: new Abstract: Using a diffusion model for parallel drafting is a promising approach for speculative decoding.
The paper introduces Tree Exit Verification (TEV), an exact, level‑parallel speculative decoding method that uses a single exit‑node decision and a bonus‑token decision to verify draft continuations. By deriving a canonical exit law, the authors show that TEV achieves the same acceptance as optimal verifiers while reducing verifier‑stage latency by 15%. They also propose Exit‑Guided Draft‑Tree Training (ExitTrain), which improves draft trees and increases average output‑block length by 13%, leading to a 14% end‑to‑end speedup over DDTree across dialogue, code, and mathematical reasoning tasks.