---
id: 20260509-T0-03
title: "LLM 计算“偷懒”新思路：让简单 Token 跳过层处理"
title_en: "New Research Enables Tokens to Skip Layers for Faster LLM Inference"
url: https://ai.daily.yangsir.net/daily/20260509-T0-03
issue_date: 2026-05-09
publish_date: 2026-05-08T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.05222
---

# LLM 计算“偷懒”新思路：让简单 Token 跳过层处理

传统 Transformer 架构对所有 Token 都执行相同层数的处理。最新研究提出了一种 Token-Selective Attention (TSA) 机制，通过在连续 Transformer 块之间的残差连接上引入可学习的 Token 门控路由，动态决定计算深度。该方法可根据上下文难度分配计算资源，在不牺牲输出质量的前提下提高推理效率。

## English Version

**New Research Enables Tokens to Skip Layers for Faster LLM Inference**

Standard transformers apply the same depth to every token. New research presents Token-Selective Attention (TSA), a learned per-token gate on residual updates that allows tokens to skip layers based on contextual difficulty. This approach dynamically routes computation, accelerating LLM inference without sacrificing quality.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.05222)

**详情页**：https://ai.daily.yangsir.net/daily/20260509-T0-03

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