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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%.
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...
arXiv:2608. 13524v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel.
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.
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.
TreeWY introduces a speculative verification method for Gated DeltaNet (GDN) hybrid models that eliminates the need for per-draft-state snapshots. By applying a tree‑structured WY transform to the gated delta rule, each draft node’s output is computed with a single triangular solve, and only the accepted state is reconstructed on commit. Benchmarks on Qwen3.5 35B and 397B show reduced memory pressure, higher throughput, and lower time‑to‑first‑token in memory‑bound scenarios, while enabling wider, higher‑acceptance draft trees.