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1 accepted papers

2025

Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo

NeurIPS 2025spotlight

As we scale to more massive machine learning models, the frequent synchronization demands inherent in data-parallel approaches create significant slowdowns, posing a critical challenge to further scaling. Recent work develops an approach (DiLoCo) that relaxes synchronization demands without compromi…

Cited by 0SourceScholar