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Review regressions before expanding the test." ], "measure": "Accepted draft length, tokens per second, tail latency, output quality, and total GPU cost.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.29013", "title": "Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding", "summary": "This is an evaluation-methodology finding, not a new decoding method: it replaces the standard practice of crediting draft-training objectives (token-level distillation, verification-gated distillation) with acceptance gains measured on externally written responses, requiring instead a baseline where the target model greedily regenerates the training corpus. 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