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Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding

arXiv — cs.CL•Tuesday, November 4, 2025 at 5:00:00 AM
NeutralArtificial Intelligence

Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding

A recent study on arXiv introduces a method called Test-time Scaling, which aims to improve the performance of large language models by optimizing resource allocation during inference. The research focuses on Best-of-N sampling, a technique that enhances the search for better solutions from model distributions. However, the study highlights the challenges associated with the cost-performance trade-off of this method, indicating that while it has potential, further exploration is needed to fully understand its efficiency. This research is significant as it could lead to advancements in how language models operate, making them more effective in real-world applications.
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