Competition-Level Code Generation with AlphaCode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian, Schrittwieser, R\'emi Leblond, Tom Eccles, James Keeling, Felix Gimeno,, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor, Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal

TL;DR
AlphaCode is a novel AI system that generates competitive programming solutions, demonstrating significant improvements in solving complex problems and achieving top rankings in large-scale competitions.
Contribution
The paper introduces AlphaCode, a new code generation system that leverages large-scale training, transformer architectures, and extensive sampling to solve complex programming problems.
Findings
AlphaCode achieved an average top 54.3% ranking in Codeforces competitions.
Key components include a large competitive programming dataset and efficient model sampling.
The system demonstrates the ability to generate solutions for complex, unseen problems.
Abstract
Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist programmers or even generate programs independently could make programming more productive and accessible, yet so far incorporating innovations in AI has proven challenging. Recent large-scale language models have demonstrated an impressive ability to generate code, and are now able to complete simple programming tasks. However, these models still perform poorly when evaluated on more complex, unseen problems that require problem-solving skills beyond simply translating instructions into code. For example, competitive programming problems which require an understanding of algorithms and complex natural language remain extremely challenging. To address this gap, we introduce AlphaCode, a system for code generation that can create novel solutions to these problems that require deeper…
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Code & Models
Videos
Competition-Level Code Generation with AlphaCode (Paper Review)· youtube
