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Aggregate-score leaderboards can mislead, as they fail to predict agent performance in real-world scenarios, revealing a critical flaw in current evaluation practices.
QGF achieves superior performance in reinforcement learning by optimizing policies solely at test time, sidestepping the instability of traditional training methods.
