Large Language Monkeys: Scaling Inference Compute with Repeated Sampling Article Swipe
Bradley Brown
,
Jordan Juravsky
,
Ryan Ehrlich
,
Ronald Clark
,
Quoc V. Le
,
Christopher Ré
,
Azalia Mirhoseini
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.21787
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.21787
Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit models to making only one attempt at a 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.21787
- https://arxiv.org/pdf/2407.21787
- OA Status
- green
- Cited By
- 8
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4402733601Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2407.21787Digital Object Identifier
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Large Language Monkeys: Scaling Inference Compute with Repeated SamplingWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-31Full publication date if available
- Authors
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Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia MirhoseiniList of authors in order
- Landing page
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https://arxiv.org/abs/2407.21787Publisher landing page
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https://arxiv.org/pdf/2407.21787Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2407.21787Direct OA link when available
- Concepts
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Inference, Scaling, Computer science, Sampling (signal processing), Statistics, Econometrics, Artificial intelligence, Mathematics, Computer vision, Geometry, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 7, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.technique | 46 |
| abstract_inverted_index.translate | 135 |
| abstract_inverted_index.verified, | 129 |
| abstract_inverted_index.collection | 186 |
| abstract_inverted_index.inference, | 20 |
| abstract_inverted_index.log-linear | 100 |
| abstract_inverted_index.magnitude. | 87 |
| abstract_inverted_index.repeatedly | 48 |
| abstract_inverted_index.suggesting | 110 |
| abstract_inverted_index.verifiers, | 175 |
| abstract_inverted_index.dramatically | 11 |
| abstract_inverted_index.performance. | 138 |
| abstract_inverted_index.relationship | 90 |
| abstract_inverted_index.automatically | 128 |
| abstract_inverted_index.capabilities. | 14 |
| abstract_inverted_index.exponentiated | 107 |
| abstract_inverted_index.outperforming | 165 |
| abstract_inverted_index.single-sample | 167 |
| abstract_inverted_index.Interestingly, | 88 |
| abstract_inverted_index.inference-time | 114 |
| abstract_inverted_index.state-of-the-art | 168 |
| abstract_inverted_index.DeepSeek-Coder-V2-Instruct | 153 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |