Inference
Best-of-N Sampling
A technique where you generate N candidate answers to the same prompt and keep the one a scorer ranks highest.
In depth
A technique where you generate N candidate answers to the same prompt and keep the one a scorer ranks highest. The scorer can be a trained verifier, a reward model, unit tests, or an LLM judge. OpenAI's GSM8K paper used this pattern, generating many solutions per math problem and selecting the top one by a trained verifier, which substantially improved accuracy. Cost grows linearly with N, so it pays off most when checking an answer is cheaper than producing one.
Primary source: arxiv.org
Example
In practice, developers reach for Best-of-N Sampling when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Best-of-N Sampling?
A technique where you generate N candidate answers to the same prompt and keep the one a scorer ranks highest.
Why does Best-of-N Sampling matter for AI developers?
Best-of-N Sampling sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Best-of-N Sampling?
Developers Digest publishes tutorials and videos that cover Inference topics including Best-of-N Sampling. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
pass@k
An evaluation metric that counts a problem as solved if at least one of k sampled attempts passes, usually checked by unit tests.
Inference
Prefill
The first phase of LLM inference, where the model processes the entire input prompt in parallel and builds the KV cache before producing any output.
Inference
Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
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