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For years, models have been trained through brute repetition. We repeatedly show them the same examples, trusting that sufficient exposure will eventually lead to understanding. It is methodical, measurable, and incredibly expensive.
A new approach called GAIN-RL (Geometry-Aware Intrinsic Network for Reinforcement Learning) suggests that models may not actually need all that repetition. Hidden within their internal representations lies a geometric signal strong enough to tell them what they have learned, and what still challenges them.
