aiqbench analysis
This research demonstrates that training objectives like distillation and gating lose significant performance gains when the target model regenerates tokens during speculative decoding, effectively nullifying benefits from external responses. The study reveals this shrinkage is robust across various hyperparameters and corpora, suggesting that future evaluations must compare new methods against a baseline where the target rewrites its own output rather than accepting externally drafted tokens.
Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding
arXiv:2610.29013v1 Announce Type: cross Abstract: Speculative decoding accelerates large language models by verifying drafted tokens in parallel. New draft-training objectives are usually credited with the acceptance they add over cross-entropy on externally written responses, yet many pipelines let the target rewrite them. We ask how much of that gain survives. With a vision-language target and a block drafter, we train three objectives on four versions of one corpus: cross-entropy on corpus tokens, token-level distillation from the target, and distillation gated to positions that would survive verification. Distillation and the gate add large gains on external responses, but after greedy regeneration, distillation adds a smaller gain and the gate little or none. The shrinkage persists across data orders, position weights, verifiers, decoding temperatures and, for distillation, initializations and learning rates. Most of it remains at matched supervised length and at each objective's best measured training length. A second vision corpus shows the shrinkage, as does a text-only replication, for distillation also with an autoregressive drafter. Objective gains should therefore be measured against a target-regenerated baseline and across training lengths. Our code is available at https://github.com/js-lee-AI/TargetRegeneration.
Abstract:Speculative decoding accelerates large language models by verifying drafted tokens in parallel. New draft-training objectives are usually credited with the acceptance they add over cross-entropy on externally written responses, yet many pipelines let the target rewrite them. We ask how much of that gain survives. With a vision-language target and a block drafter, we train three objectives on four versions of one corpus: cross-entropy on corpus tokens, token-level distillation from the target, and distillation gated to positions that would survive verification. Distillation and the gate add large gains on external responses, but after greedy regeneration, distillation adds a smaller gain and the gate little or none. The shrinkage persists across data orders, position weights, verifiers, decoding temperatures and, for distillation, initializations and learning rates. Most of it remains at matched supervised length and at each objective's best measured training length. A second vision corpus shows the shrinkage, as does a text-only replication, for distillation also with an autoregressive drafter. Objective gains should therefore be measured against a target-regenerated baseline and across training lengths. Our code is available at this https URL.
19 pages, 6 figures, 15 tables. Code: this https URL
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2610.29013 [cs.CL]
(or arXiv:2610.29013v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.29013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 289 words