---
id: 20260502-T0-02
title: "AutoSP通过编译器序列并行训练长上下文LLM"
title_en: "AutoSP Enables Long-Context LLM Training via Compiler Parallelism"
url: https://ai.daily.yangsir.net/daily/20260502-T0-02
issue_date: 2026-05-02
publish_date: 2026-05-01T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2604.27089
---

# AutoSP通过编译器序列并行训练长上下文LLM

arXiv论文AutoSP提出基于编译器的序列并行方法，解决长上下文LLM训练难题。该技术通过优化十万到百万级token的处理效率，突破现有训练库的抽象限制，使长文档处理更加高效。研究为大规模语言模型的性能提升提供了新路径。

## English Version

**AutoSP Enables Long-Context LLM Training via Compiler Parallelism**

arXiv paper AutoSP proposes a compiler-based sequence parallelism method to solve long-context LLM training challenges. By optimizing processing efficiency for 100k-1M+ tokens, the method overcomes limitations in existing training libraries, enabling more efficient long-document processing. The research offers a new path for improving large language model performance.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2604.27089)

**详情页**：https://ai.daily.yangsir.net/daily/20260502-T0-02

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