Preliminary results from a geo-distributed LLM training prototype that treats networking constraints as first-order design concerns are presented, motivating adaptive networking support for synchronization, compression, placement, telemetry, and recovery in geo-distributed LLM training.
Ziyue Luo, Jiaxuan Cai, Cedric Le Denmat et al.· Conference on Applications,...· 0 citations
A nonasymptotic risk bound is established that disentangles pretraining representation error from labeled-sample complexity, formally quantifying the benefit of large-scale unlabeled data for downstream knowledge prediction.
Jifan Zhang, Mikl'os Z. R'acz, Suqi Liu· arXiv.org· 0 citations
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