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-            We evaluate CarBoN on MATH-500 and AIME-2024 benchmarks across multiple language models. The results demonstrate that CarBoN consistently improves upon standard Best-of-\(N\) sampling, with calibrated accuracy at \(N=64\) matching or exceeding uncalibrated results at \(N=256\)—a \(4\times\) reduction in rollout budget.
+            We evaluate CarBoN on MATH-500 and AIME-2024 benchmarks across multiple language models. The results demonstrate that CarBoN consistently improves upon standard Best-of-\(N\) sampling, with calibrated accuracy at \(N=64\) matching or exceeding uncalibrated results at \(N=256\) (\(4\times\) reduction in rollout budget).
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