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Output-layer gradients alone are sufficient for effective post-training data selection, slashing feature-extraction costs by up to 10x.
Zero model disagreement is achievable with simple techniques like stacking, boosting, and architecture search, converging as you increase the number of models, iterations, or architecture size.
