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 reasoning. In simulated
evaluations on recent programming competitions on the Codeforces platform,
AlphaCode achieved on average a ranking of top 54.3% in competitions with more
than 5,000 participants. We found that three key components were critical to
achieve good and reliable performance: (1) an extensive and clean competitive
programming dataset for training and evaluation, (2) large and
efficient-to-sample transformer-based architectures, and (3) large-scale model
sampling to explore the search space, followed by filtering based on program
behavior to a small set of submissions.

AlphaCode 是一个针对竞争性编程问题的系统，通过三个关键组件（专业数据集、高效转换架构和大规模模型采样）实现针对较深层次的推理并在 Codeforces 上实现了良好和可靠的表现。