Inference scaling has become one of the most effective ways to improve answer quality and accuracy in deployed LLMs. The idea is straightforward. If we are willing to spend a bit more compute, and more time at inference time (when we use the model to generate text), we can get the model to produce better answers.

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Inference-time scaling improves LLM answer quality by allocating more compute during text generation rather than training. The article categorizes different approaches including chain-of-thought prompting, self-consistency, best-of-N ranking, rejection sampling, self-refinement, and search over solution paths. Major LLM providers use these techniques, which can boost model accuracy significantly without changing model weights. The piece draws from research for a book chapter that improved base model accuracy from 15% to 52%.

Categories of Inference-Time Scaling for Improved LLM Reasoning