English(EN) Generating Pretraining Tokens from Organic Data for Data-Bound Scaling

新框架助力LLM从有限的有机数据中学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-18 04:44

研究人员开发了SynPro，一个旨在帮助大型语言模型从有限的有机数据中更有效地学习的框架。该方法使用改写和重新格式化技术以多样化的方式呈现现有数据，在不引入新信息的情况下增强学习。对400M和1.1B模型的实验表明，与标准重复相比，SynPro可以实现更有效的Token利用率，甚至在1.1B规模上优于非数据约束的Oracle。 AI

影响通过最大化从现有有机数据中学习来提高LLM的训练效率，可能减少对海量新数据集的需求。

排序理由该集群包含一篇详细介绍改进LLM预训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CL TIER_1 English(EN) · Chenyan Xiong · 2026-05-18 04:44

从有机数据生成预训练Token以实现数据约束扩展

LLM pretraining is shifting from a compute-bound to a data-bound regime, where available human (organic) text falls far short of scaling demands. However, reaching the data-bound regime does not mean the model has fully utilized its organic corpus. In this paper, we introduce Syn…

本文如何被排名

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从有机数据生成预训练Token以实现数据约束扩展

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