长上下文草拟无需长上下文草拟模型

arXiv cs.LG 论文

摘要

该论文论证冻结的短上下文草稿模型无需重新训练即可用于超长上下文的推测解码,通过限制注意力缓存和重置旋转位置编码两种推理时机制,使其速度优势在长达两百万 token 的上下文中保持稳定。

arXiv:2610.20865v1 Announce Type: new Abstract: Speculative decoding pairs a cheap drafter with parallel verification against the target. At ultra-long context it stops paying, because a drafter that attends over the full prefix inherits the target's context-dependent cost, so the speedup it was meant to provide first erodes and then reverses. The standard response is to build a long-context drafter, by training or fine-tuning one, or by using a multi-token-prediction head shipped inside the target checkpoint. We argue this is unnecessary, because the drafters in use are target-conditioned, reading hidden states the target computed over the entire prefix, so distant context already reaches them without their own attention, and verification corrects what they miss. We therefore freeze released short-context drafters and run them far past their training length under two inference-time mechanisms, one bounding the attention cache they see, which fixes their cost, and one re-basing their rotary position indices into the trained range. No weight is touched and the only tunable saturates early, so one vLLM-side configuration spans 4k to two million tokens, costing at most 0.16x of speedup at short length. We prove when a common index shift leaves the drafter's computation exactly unchanged, and decompose acceptance into two measurable gaps with sharp bounds. Whatever speedup a frozen drafter delivers at short context, it retains out to two million tokens, across three target-drafter pairs and five corpora. The claim concerns the flatness rather than the multiple, and removing either mechanism eliminates it. The gain is conditional on the speculative cost per emitted token staying below the matched target-only cost.
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# Long-Context Drafting Does Not Require Long-Context Drafters
Source: [https://arxiv.org/abs/2610.20865](https://arxiv.org/abs/2610.20865)
[View PDF](https://arxiv.org/pdf/2610.20865)

> Abstract:Speculative decoding pairs a cheap drafter with parallel verification against the target\. At ultra\-long context it stops paying, because a drafter that attends over the full prefix inherits the target's context\-dependent cost, so the speedup it was meant to provide first erodes and then reverses\. The standard response is to build a long\-context drafter, by training or fine\-tuning one, or by using a multi\-token\-prediction head shipped inside the target checkpoint\. We argue this is unnecessary, because the drafters in use are target\-conditioned, reading hidden states the target computed over the entire prefix, so distant context already reaches them without their own attention, and verification corrects what they miss\. We therefore freeze released short\-context drafters and run them far past their training length under two inference\-time mechanisms, one bounding the attention cache they see, which fixes their cost, and one re\-basing their rotary position indices into the trained range\. No weight is touched and the only tunable saturates early, so one vLLM\-side configuration spans 4k to two million tokens, costing at most 0\.16x of speedup at short length\. We prove when a common index shift leaves the drafter's computation exactly unchanged, and decompose acceptance into two measurable gaps with sharp bounds\. Whatever speedup a frozen drafter delivers at short context, it retains out to two million tokens, across three target\-drafter pairs and five corpora\. The claim concerns the flatness rather than the multiple, and removing either mechanism eliminates it\. The gain is conditional on the speculative cost per emitted token staying below the matched target\-only cost\.

## Submission history

From: Aref Jafari \[[view email](https://arxiv.org/show-email/f3e18d34/2610.20865)\] **\[v1\]**Wed, 14 Oct 2026 19:54:59 UTC \(86 KB\)

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