Context lengths of language models (LMs) have dramatically increased, driven by the demands for in-context learning, self-improvement, and long-horizon agentic workflows. Existing long-context corpora, however, are dominated by books, academic articles, and code repositories, which are finite resources and often scarce in long-distance dependencies. In this work, we introduce OctoLong, a context e

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2608.05141v1)获取最准确的信息。

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🔗 **原文链接**: [OctoLong: Mid-Training On Cross-Repository Code Contexts Enh](https://arxiv.org/abs/2608.05141v1)
🏷️ **转载来源**: ArXiv cs.AI
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
👤 作者: Indraneil Paul, Falko Helm, Goran Glavaš, Iryna Gurevych

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🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
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> _转载自 ArXiv cs.AI，内容版权归原作者所有_

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⏱️ 2026-08-06 14:03