Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

ICLR 2025 Conference Submission4004 Authors

Keywords: Inference-Time Compute, Large Language Models

TL;DR: We investigate sampling many solutions from an LLM when solving a problem, showing that this simple approach can be scalable and effective.

Abstract: Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit the amount of compute to only one attempt per problem. Here, we explore inference compute as another axis for scaling, using the simple technique of repeatedly sampling candidate solutions from a model. Across multiple tasks and models, we observe that coverage – the fraction of problems that are solved by any generated sample – scales with the number of samples over four orders of magnitude. Interestingly, the relationship between coverage and the number of samples is often log-linear and can be modelled with an exponentiated power law, suggesting the existence of inference-time scaling laws. In domains like coding and formal proofs, where answers can be automatically verified, these increases in coverage directly translate into improved performance. When we apply repeated sampling to SWE-bench Lite, the fraction of issues solved with DeepSeek-Coder-V2-Instruct increases from 15.9% with one sample to 56% with 250 samples, outperforming the single-sample state-of-the-art of 43%. In domains without automatic verifiers, we find that common methods for picking from a sample collection (majority voting and reward models) plateau beyond several hundred samples and fail to fully scale with the sample budget.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 4004