Try Many Answers: How Repeated Sampling Lets AI Solve More Problems
Most AI models usually give one answer and move on.
But if you let them try again and again, they often find a right answer later.
This simple trick, called repeated sampling, raises the share of solved tasks, or coverage, by a lot — more tries means more wins, usually in a smooth, predictable way.
On tasks that can be checked automatically, like coding or formal proofs, the gain is dramatic.
For example a model that solved 15.
9% of issues with one try solved about 56% with 250 samples.
That jump shows extra compute at inference time can beat improving the model alone.
When answers can be verified automatically, every extra correct sample counts directly.
But in places without auto checks, grabbing the best answer from many outputs hits limits.
Methods like voting or reward scores stop improving after some hundreds of tries, so new ideas needed.
Still, the message is clear: giving models more tries is a powerful yet simple way to get better results, and it changes how we think about using AI.
Read article comprehensive review in Paperium.net:
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
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