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README.md
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- Best of N: By leveraging a combination of response sampling and Best-of-N strategies, we choose the response of top score judged by reward model, yielding better results with spending more inference time. For example, Qwen2-Math-1.5B-Instruct obtains 79.9 on MATH in RM@8 setting and even surpasses the performance of Qwen2-Math-7B-Instruct 75.1 with greedy decoding.
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- Comparasion with majority voting (Maj@N): RM@N scores are substantially better than Maj@N scores aross almost all benchmarks and models.
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## Model Details
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- Best of N: By leveraging a combination of response sampling and Best-of-N strategies, we choose the response of top score judged by reward model, yielding better results with spending more inference time. For example, Qwen2-Math-1.5B-Instruct obtains 79.9 on MATH in RM@8 setting and even surpasses the performance of Qwen2-Math-7B-Instruct 75.1 with greedy decoding.
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- Comparasion with majority voting (Maj@N): RM@N scores are substantially better than Maj@N scores aross almost all benchmarks and models.
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## Model Details
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