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Overparameterization doesn't just add parameters; it warps the loss landscape geometry itself, making well-conditioned minima easier to find.
Models are substantially better at pairwise self-verification than independent scoring, unlocking a more efficient and accurate approach to test-time scaling for complex reasoning.
