Best-of-N Sampling (Parallel Scaling)
Parallel scaling, also known as best-of-N sampling, is a strategy that involves generating K independent candidate solutions by running a base LLM multiple times. During this generation process, the sampling temperature can be adjusted to control the diversity of the outputs. After the candidates are created, a verifier evaluates each of the K complete solutions, and the one with the highest score is selected as the final answer. This method is conceptually analogous to using a reward model to select the best option from a set of sampled outputs.
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References
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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