<ul><li>Researchers have developed the Easy-to-Hard Generalization (E2H) methodology to tackle alignment issues in complex tasks without relying on human feedback.</li><li>The methodology involves Process-Supervised Reward Models (PRMs), Easy-to-Hard generalization, and Iterative Refinement.</li><li>The E2H methodology enables AI models to shift from human-feedback-dependent to reduced human annotations.</li><li>The method demonstrates significant improvements in performance and reduces the need for human-labeled data on complex tasks.</li></ul>

How AI Models Learn to Solve Problems That Humans Can’t

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