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The Skaling law slashes loss estimation errors by up to 3x and reduces compute needs by 90% for reliable model performance predictions.
Standard MCQA metrics can mislead evaluations by over 2 points due to phrasing sensitivity, but ParaEval cuts this gap to under 1 point, revealing true model capabilities.
