Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining

ICLR 2025 Conference Submission13352 Authors

Keywords: sample reweighing, large language models, pretraining

TL;DR: We propose new loss-based sample reweighing techniques for improved efficiency and effectiveness of LLMs pretraining.

Abstract:

Pretraining large language models (LLMs) on vast and heterogeneous datasets is crucial for achieving state-of-the-art performance across diverse downstream tasks. However, current training paradigms treat all samples equally, overlooking the importance or relevance of individual samples throughout the training process. Existing reweighting strategies, which primarily focus on group-level data importance, fail to leverage fine-grained instance-level information and do not adapt dynamically to individual sample importance as training progresses. In this paper, we introduce novel algorithms for dynamic, instance-level data reweighting aimed at improving both the efficiency and effectiveness of LLM pretraining. Our methods adjust the weight of each training sample based on its loss value in an online fashion, allowing the model to dynamically focus on more informative or important samples at the current training stage. In particular, our framework allows us to systematically devise reweighting strategies deprioritizing redundant or uninformative data, which we find tend to work best.
Furthermore, we develop a new theoretical framework for analyzing the impact of loss-based reweighting on the convergence of gradient-based optimization, providing the first formal characterization of how these strategies affect convergence bounds. We empirically validate our approach across a spectrum of tasks, from large-scale LLM pretraining to smaller-scale linear regression problems, demonstrating that loss-based reweighting can lead to faster convergence and improved performance.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 13352