Demystifying Data Organization for Enhanced LLM Training

Problem
This preprint addresses a significant gap in the literature regarding the organization of training data for Large Language Models (LLMs). While data selection strategies have been extensively studied, the systematic impact of data organization on training efficiency remains underexplored. Current LLM training paradigms often involve limited epochs, which can lead to suboptimal learning outcomes. The authors aim to elucidate how strategic data organization can enhance training performance, thereby filling this critical void in the existing body of work.

Method
The authors propose four guidelines for optimizing data organization: Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, and Local Diversity. They introduce two novel data ordering methods, STR (Sample-based Training Reordering) and SAW (Sample-based Adaptive Weighting), which leverage pre-computed sample-level scores to minimize additional computational overhead. The methods are designed to be applicable during both pre-training and supervised fine-tuning (SFT) stages. The experiments were conducted across various model scales and data sizes, although specific details regarding the architecture, loss functions, and training compute were not disclosed.

Results
The proposed data ordering methods, STR and SAW, were evaluated against standard baselines on multiple benchmarks. The results indicate a significant improvement in training stability and performance metrics, although specific headline numbers (e.g., accuracy, loss reduction) were not detailed in the abstract. The authors report that their methods consistently outperform traditional data organization techniques, demonstrating robustness across different model configurations and datasets.

Limitations
The authors acknowledge that their study is limited by the reliance on pre-computed sample-level scores, which may not generalize across all datasets or tasks. Additionally, the paper does not explore the potential computational costs associated with generating these scores, nor does it address the scalability of the proposed methods in extremely large datasets. The lack of detailed performance metrics in the abstract also limits the ability to fully assess the effectiveness of the proposed methods compared to existing approaches.

Why it matters
This work has significant implications for the training of LLMs, particularly in optimizing data organization to enhance learning efficiency. By formalizing guidelines and introducing novel data ordering methods, the authors provide a framework that can be adopted in future LLM training protocols. This could lead to more effective utilization of training data, ultimately improving model performance and reducing the computational resources required for training. The findings encourage further exploration into data organization strategies, potentially influencing future research directions in LLM training methodologies.

Authors: Yalun Dai, Yangyu Huang, Tongshen Yang, Yonghan Wang, Xin Zhang, Wenshan Wu, Qihao Zhao, Hao Li et al.
Source: arXiv:2605.30334
URL: https://arxiv.org/abs/2605.30334v1