Competition-Level Code Generation with AlphaCode

AlphaCode (Science, February 2022) was DeepMind's entry into competitive programming — a domain where problems require understanding complex algorithmic requirements, not just predicting the next token in a sequence. The system achieved median performance on Codeforces competitive programming contests, placing roughly in the top 50th percentile among human competitors. The paper's key technical contribution is its sampling and filtering approach: generate a large set of candidate solutions (up to 1 million), then use a filtering model trained on problem-solution pairs to select the most promising subset to submit. AlphaCode also uses a large Transformer pretrained on GitHub code, then fine-tuned on competitive programming problems. The paper demonstrates that AI can tackle problems requiring genuine algorithmic reasoning — not just boilerplate pattern completion. It also reveals the gap that still exists: AlphaCode performs well on classic algorithmic problems but struggles with novel problems requiring creative insight. Published before the ChatGPT era, it established that large code models could do algorithmic reasoning and created the research baseline for subsequent systems like AlphaCode 2.