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Demystifying Data Organization for Enhanced LLM Tra... | AI Research

Key Takeaways

  • Demystifying Data Organization for Enhanced LLM Training explores how the order in which training data is presented to a Large Language Model (LLM) significa...
  • 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.
  • We identify and formalize four key guidelines for optimizing data organization: Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, and Local Diversity.
  • Guided by them, we introduce two novel data ordering methods termed STR and SAW.
Paper AbstractExpand

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 Scheduling, Curriculum Continuity, and Local Diversity. Guided by them, we introduce two novel data ordering methods termed STR and SAW. Extensive experiments across different model scales and data sizes, encompassing both pre-training and SFT stages, validate the effectiveness of our summarized guidelines. They also demonstrate the robustness of our proposed data ordering methods in enhancing the stability and performance of LLM training. Github Link: this https URL

Demystifying Data Organization for Enhanced LLM Training explores how the order in which training data is presented to a Large Language Model (LLM) significantly impacts its performance. While much research has focused on selecting the right data, this paper highlights that the sequence of that data is a critical, yet often overlooked, factor in training efficiency. By reusing pre-computed scores—such as quality or difficulty metrics—the authors propose a way to organize training data that improves model stability and performance without requiring extra computational power.

Four Pillars of Data Organization

The researchers identified four core guidelines to optimize how data is sequenced during training:

  • Boundary Sharpening: This suggests starting training with simpler, lower-score data to stabilize the model, and finishing with higher-score, more complex data to boost performance on difficult tasks.

  • Cyclic Scheduling: To prevent the model from forgetting early, basic knowledge as it moves toward complex data, this approach periodically reintroduces a mix of data across the entire score spectrum.

  • Curriculum Continuity: This focuses on maintaining smooth transitions between different types of data. By avoiding abrupt jumps in data attributes, the model experiences less "shock," which helps keep the optimization process stable.

  • Local Diversity: This encourages mixing diverse samples within small batches. By preventing the model from seeing only identical data points at once, it reduces the risk of overfitting and helps the model learn more generalizable features.

Practical Ordering Strategies

To put these guidelines into practice, the authors developed two primary methods: Stair Ordering (STR) and Saw Ordering (SAW). Both methods use pre-computed scores to arrange data into a structured sequence. STR focuses on maintaining a global progression while periodically folding in earlier data to ensure knowledge retention. SAW builds on this by adding a "zig-zag" mechanism that ensures smoother transitions between different data segments, further enhancing the stability of the training process.

Performance and Efficiency

The study demonstrates that these organized sequences consistently outperform traditional random data presentation and standard curriculum learning. Experiments across various model scales and tasks—including pre-training and supervised fine-tuning—show that these methods lead to more stable training trajectories and better overall accuracy. Because these strategies rely on scores that are already generated during the data selection phase, they provide these performance gains with minimal additional computational overhead, making them a highly efficient way to improve LLM development.

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