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MOSAIC jointly selects a sparse-MoE architecture, token budget, and parallel layout under a fixed cluster and training window.
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A practitioner's account of what pretraining research can predict, where current methods break, and which questions remain open.
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AI raised individual throughput but coordination overhead stayed fixed.
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A practical framework for comparing research projects by probability of success, effect size, and weighted reach.
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Deriving model size and training tokens from a fitted scaling law, then extending the calculation to repeated data, inference demand, and cluster efficiency.
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Why we built GluonNLP to make NLP experiments easier to reproduce, maintain, and reuse.