Demystifying Data Organization for Enhanced LLM Training
Yalun Dai, Yangyu Huang, Tongshen Yang, Yonghan Wang, Xin Zhang, Wenshan Wu, Qihao Zhao, Hao Li, Yuanyuan Gao, Kim-Hui Yap, Scarlett Li
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Key claim
Data organization significantly enhances LLM training performance.
This paper explores how data organization can improve the training of large language models. It introduces two new methods for data ordering that significantly enhance training stability and performance. The findings suggest that strategic data organization is crucial for optimizing LLM training efficiency.
In plain English
This paper explores how data organization can improve the training of large language models. It introduces two new methods for data ordering that significantly enhance training stability and performance. The findings suggest that strategic data organization is crucial for optimizing LLM training efficiency.
The paper introduces novel data ordering methods and guidelines that enhance LLM training efficiency.
The claims are supported by extensive experiments across various model scales and data sizes.
Deep reliability assessment
The experiments support that score-based sample ordering can improve training stability and benchmark averages over random order and simple curriculum baselines in the tested pre-training and SFT setups. The broader claim that these four guidances generally optimize LLM training is stronger than the evidence, because the method depends on the quality of pre-computed sample scores and is evaluated on limited model scales, datasets, and language-only settings.
Reproducibility
Yes. The paper provides a GitHub repository and uses publicly available datasets/model architectures, including FineWeb-Edu, DeepMath-103K, and OpenCodeInstruct, though reproduction still depends on access to the exact pre-computed sample-level scores and training configurations.
Discussion questions
- 1.Does the method work because of universal learning dynamics, or because the chosen sample-level scores already encode dataset-specific quality and difficulty biases?
- 2.For teams training or fine-tuning models in SEA with constrained compute, is data reordering worth implementing before more standard interventions like better filtering, deduplication, or data mixing?
- 3.What result would falsify the paper's core claim: failure on larger frontier-scale models, failure with different scoring functions, or no gains when controlling for batch composition and token distribution?
Key figure
Figure 1 compares Random, Curriculum Learning, and the proposed SAW ordering across model sizes, showing SAW creates a more structured score-index curriculum and achieves higher average accuracy from 160M to 1.7B parameters.