arXiv:2604.09258v2 Announce Type: replace Abstract: The foundational capabilities of large language models are acquired during pretraining on internet-scale, highly heterogeneous data mixtures. In this work, we investigate an interesting geometric question regarding the converged state of pretraining: Does the model converge to a common minimizer across all data sources (e.g., \cref{fig:cwa_illustration:close}), or merely a minimizer of the summed loss (e.g., \cref{fig:cwa_illustration:distant})? We hypothesize that the geometric "closeness" of task-specific minima is intrinsically linked to d
The continuous scaling of large language models necessitates deeper understanding of pretraining dynamics to optimize their foundational capabilities and downstream generalization.
This research provides insights into a fundamental aspect of AI model architecture and training, directly impacting performance and efficiency of future large language models.
A better understanding of common minima in pretraining could lead to more robust and generalizable AI models, improving their applicability across diverse tasks.
- · AI researchers
- · Large language model developers
- · Companies leveraging LLMs
- · AI models with suboptimal generalization
- · Less efficient training methodologies
Improved model generalization could reduce the need for extensive fine-tuning on specific downstream tasks.
More robust foundation models might accelerate the development of autonomous AI agents and complex AI applications.
Enhanced generalization capabilities could reduce the energy footprint and computational resources required for deploying and adapting AI across a wider range of industries, indirectly impacting aspects of the 'energy-bottleneck' narrative.
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Read at arXiv cs.LG