SPIN Unprocessed October 22, 2026 ai_technology research
Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding
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arXiv:2610.29013v1 Announce Type: new 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 toke
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