AlphaCode: An AI System for Competitive Programming
Solving problems and achieving breakthroughs in competitive programming has always been a challenge for AI systems. While machine learning has made significant progress in generating and understanding textual data, problem-solving abilities have remained limited to basic math and programming problems, or simply copying existing solutions.
At DeepMind, we developed AlphaCode, a system that writes computer programs at a competitive level. AlphaCode uses transformer-based language models to generate code on a large scale, and then carefully filters down to a small set of promising programs.
Our system was put to the test on Codeforces, a popular platform for coding competitions with thousands of participants worldwide. We evaluated AlphaCode on 10 recent contests, all newer than our training data. The results were impressive – AlphaCode ranked at the level of the median competitor. This is the first time an AI code generation system has reached a competitive level of performance in programming competitions.
To assist others in building on our results, we have released our dataset of competitive programming problems and solutions on GitHub. This dataset includes extensive tests to ensure the correctness of the programs, a feature that is lacking in current datasets. We hope this benchmark will inspire further innovation in problem-solving and code generation.
Competitive programming is a popular activity where programmers participate in coding competitions to showcase their skills and gain experience. These competitions involve solving complex problems within a limited timeframe. Such problems range from urban planning to board game strategies. Employers often use these competitions as a way to recruit talented software engineers.
The problem-solving abilities required for these competitions go beyond what current AI systems can achieve. However, by combining transformer models with large-scale sampling and filtering, we have made significant progress in solving a wider range of problems. Our model is pre-trained on public GitHub code and fine-tuned on our competitive programming dataset.
During evaluation, we generate a large number of C++ and Python programs for each problem and then filter them down to a small set of 10 candidate programs for assessment. This automated system eliminates the need for trial and error, debugging, and manual submission.
AlphaCode’s performance in competitive programming is a major step forward. While it may not win competitions, it demonstrates the potential of AI systems in problem-solving. We believe that our results will inspire the competitive programming community to explore new possibilities.
To further improve AI problem-solving capabilities, we will continue to research and develop tools that enhance programming productivity. Our ultimate goal is to create a problem-solving AI that can benefit humanity as a whole.
Visit alphacode.deepmind.com to explore AlphaCode’s solutions and learn more about the model.