Reliable deductive reasoning in AI requires replacing average-case statistical objectives with the exact learning criterion of universal correctness, a thesis supported by sample-complexity lower bounds showing statistical learners need exponentially many examples for exact identification.
Instruction fine-tuning: Does prompt loss matter? InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 22771–22795,
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Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Reliable deductive reasoning in AI requires replacing average-case statistical objectives with the exact learning criterion of universal correctness, a thesis supported by sample-complexity lower bounds showing statistical learners need exponentially many examples for exact identification.