AI RESEARCH
FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost
arXiv CS.LG
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ArXi:2604.24073v1 Announce Type: new Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictions based on these derived interests. The inherent heterogeneity in data characteristics frequently result in substantial under-utilization of computational resources during large-scale