---
id: 20260722-T0-04
title: "SpecLA：线性注意力模型的推测解码加速方案"
title_en: "SpecLA: Fast Speculative Decoding for Linear-Attention Models"
url: https://ai.daily.yangsir.net/daily/20260722-T0-04
issue_date: 2026-07-22
publish_date: 2026-07-21T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2607.16673
---

# SpecLA：线性注意力模型的推测解码加速方案

arXiv新论文提出SpecLA，一种针对线性注意力模型的高效推测解码方法。线性注意力模型使用循环状态替代KV缓存，但自回归解码仍逐token读写状态。SpecLA通过一次性验证多个候选token，大幅减少状态读取/写入次数。实验显示，该方法在保持生成质量的同时将解码速度提升2-3倍。

## English Version

**SpecLA: Fast Speculative Decoding for Linear-Attention Models**

A new arXiv paper proposes SpecLA, an efficient speculative decoding method for linear-attention models. It verifies multiple draft tokens in one pass, reducing state read/write operations. Experiments show 2-3x speedup in decoding while maintaining generation quality.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2607.16673)

**详情页**：https://ai.daily.yangsir.net/daily/20260722-T0-04

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