arXiv:2606.07954v1 Announce Type: new Abstract: Training large language models (LLMs) on heterogeneous data requires selecting minibatches that balance convergence speed with coverage across domains. Existing methods either select samples independently within each domain or rely on computationally expensive proxy models to learn continuous domain weights. We propose PartitionSel, a cross-domain minibatch selection approach that maximizes a validation-guided gradient-matching utility under per-domain budgets encoded as a partition-matroid constraint. By coupling the per-domain budgets through a
The increasing scale and heterogeneity of data used for training large language models necessitate more efficient and nuanced minibatch selection techniques to optimize resource utilization and model performance.
Improving minibatch selection for LLMs directly impacts training efficiency, convergence speed, and the ability to leverage diverse datasets, which is crucial for the continued advancement and application of AI.
This new method offers a more sophisticated way to balance data coverage and convergence, potentially leading to faster training times and more robust LLMs, especially in complex, multi-domain applications.
- · AI model developers
- · Cloud infrastructure providers (optimizing compute)
- · Enterprises deploying LLMs for diverse tasks
- · R&D teams using less efficient training methods
- · Legacy deep learning frameworks
More efficient and generalizable large language models become available for a wider range of applications, especially those requiring cross-domain understanding.
Reduced computational costs for training advanced AI models could democratize access to cutting-edge AI development, fostering innovation in new sectors.
The development of highly adaptive LLMs, trained on broad and diverse data, could accelerate the development of more capable AI agents across various industries.
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Read at arXiv cs.LG