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Exploiting cyclic symmetries in multi-index models offers a polynomial sample complexity advantage, with data augmentation proving as sample-efficient as architectural weight sharing.
Forget knowledge distillation: pre-training ColBERT from scratch on public data alone beats models distilled from stronger, closed-source single-vector baselines.
