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It is shown that this assumption can fail in a full 30B mixture-of-experts training pipeline, and the checkpoints that perform better after the full downstream training stack also have higher solution density, i.e., retain downstream performance under local weight perturbations.
A family of premetrics that capture different degrees of structural similarity between graphs are introduced and relate these similarities to generalization, and consequently, the performance of expressive GNNs are related.
The experiments show that the trade-off between inference efficiency and translation quality depends not only on the quantization format, but also on the choice of text chunking strategy, as well as on the choice of text chunking strategy.
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