We study the continual pretraining recipe for scaling language models'
context lengths to 128K, with a focus on data engineering. We hypothesize that
long context modeling, in particular \textit{the ability to utilize information
at arbitrary input locations}, is a capability that is mostly already acquired
through large-scale pretraining, and that this capability can be readily
extended to contexts substantially longer than seen during training~(e.g., 4K
to 128K) through lightweight continual pretraining on appropriate data mixture.
We investigate the \textit{quantity} and \textit{quality} of the data for
continual pretraining: (1) for quantity, we show that 500 million to 5 billion
tokens are enough to enable the model to retrieve information anywhere within
the 128K context; (2) for quality, our results equally emphasize \textit{domain
balance} and \textit{length upsampling}. Concretely, we find that naively
upsampling longer data on certain domains like books, a common practice of
existing work, gives suboptimal performance, and that a balanced domain mixture
is important. We demonstrate that continual pretraining of the full model on
1B-5B tokens of such data is an effective and affordable strategy for scaling
the context length of language models to 128K. Our recipe outperforms strong
open-source long-context models and closes the gap to frontier models like
GPT-4 128K.

本研究探讨使用持续预训练重现将语言模型的上下文长度扩展到 128K，重点关注数据工程。我们假设长篇背景建模，特别是 “能够在任意输入位置利用信息” 的能力在大规模预训练中已经获得，而且这种能力可以通过对适当数据混合进行轻量级持续预训练来延伸到比训练中看到的上下文更长的情况（例如，从 4K 延伸到 128K）。我们研究了持续预训练的 “数量” 和 “质量”：（1）对于数量，我们表明 5 亿到 50 亿个标记足以使模型能够检索到 128K 上下文中的任何信息；（2）对于质量，我们的结果同样强调了 “领域平衡” 和 “长度上采样”。具体来说，我们发现像书籍这样的特定领域上的长数据简单上采样，这是现有工作的常见做法，会得到次优性能，而平衡的领域混合是重要的。我们证明使用 1B-50B 个标记的数据对整个模型进行持续预训练是将语言模型的上下文长度扩展到 128K 的一种有效和经济的策略。我们的方法胜过强大的开源长篇模型，并缩小了与 GPT-4 128K 等前沿模型之间的差距。