---
id: 20260627-T0-06
title: "SSM Adapter新方法：长上下文微调中注入位置决定任务适配性"
title_en: "SSM Adapters: Injection Site Determines Task Suitability in Long-Context Fine-Tuning"
url: https://ai.daily.yangsir.net/daily/20260627-T0-06
issue_date: 2026-06-27
publish_date: 2026-06-26T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2606.26290
---

# SSM Adapter新方法：长上下文微调中注入位置决定任务适配性

arXiv:2606.26290v1 发布。研究指出，传统的参数高效微调（PEFT）通常针对注意力投影层，但在需要状态累积的任务上效果不佳。新方法提出了一种基于Hankel降阶建模的SSM（状态空间模型）适配器，并发现适配器的注入位置（Injection Site）对任务表现至关重要：深层注入更适合需要长程状态累积的任务。

## English Version

**SSM Adapters: Injection Site Determines Task Suitability in Long-Context Fine-Tuning**

arXiv:2606.26290v1 released. Traditional PEFT targets attention projectors but struggles with tasks requiring state accumulation. This paper proposes SSM adapters via Hankel reduced-order modeling, revealing that the adapter injection site is critical: deeper injections are better for tasks requiring long-range sequential state accumulation.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2606.26290)

**详情页**：https://ai.daily.yangsir.net/daily/20260627-T0-06

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