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Beyond Pass@k: Breadth-Depth Metrics for Reasoning Boundaries
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We argue that on tasks with discrete answer spaces, such as math with numeric outputs, Pass@k at large k reflects the increasingly higher chance of success in the limit of the number of trials rather than genuine reasoning, and can therefore be misleading.
Large-k pass@k is a valid measure of the boundary of language-model reasoning capability.
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