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Independently trained depth slices can be recombined to match the performance of monolithic models, revealing a new avenue for efficient language model training.
Achieve zero global downtime in large-scale pre-training, even with millions of simulated chip failures, by decoupling learners and asynchronously aggregating parameter updates.
