Demystifying Data Organization for Enhanced LLM Training
Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data selection has been widely studied, the strategic data organization for enhanced training remains an underexplored area, particularly since current LLMs are often trained for only one or a few epochs. This paper systematically explores the influence of data organization on LLM training by reusing pre-computed sample-level scores originally generated for data efficiency, thereby incurring minimal additional computational overhead. We identify and formalize four key guidelines for optimizing data organization: Boundary Sharpening, Cyclic Sch...
| Authors | Yalun Dai, Yangyu Huang, Tongshen Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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