Code generation problems differ from common natural language problems - they require matching the exact syntax of the target language, identifying happy paths and edge cases, paying attention to numerous small details in the problem spec, and addressing other code-specific issues and requirements. Hence, many of the optimizations and tricks that have been successful in natural language generation may not be effective for code tasks. In this work, we propose a new approach to code generation by LLMs, which we call AlphaCodium - a test-based, multi-stage, code-oriented iterative flow, that improves the performances of LLMs on code problems. We tested AlphaCodium on a challenging code generation dataset called CodeContests, which includes competitive programming problems from platforms such as Codeforces. The proposed flow consistently and significantly improves results. On the validation set, for example, GPT-4 accuracy (pass@5) increased from 19% with a single well-designed direct prompt to 44% with the AlphaCodium flow. Many of the principles and best practices acquired in this work, we believe, are broadly applicable to general code generation tasks. Full implementation is available at: https://github.com/Codium-ai/AlphaCodium

翻译：代码生成问题与常见的自然语言问题不同——它需要匹配目标语言的精确语法、识别正向路径和边界情况、关注问题规范中的大量细节，并解决其他代码特有的问题和要求。因此，许多在自然语言生成中成功的优化技巧和策略可能对代码任务并不有效。在这项工作中，我们提出了一种新的基于大语言模型的代码生成方法，称为AlphaCodium——一种基于测试、多阶段、面向代码的迭代流程，可提升LLM在代码问题上的性能。我们在名为CodeContests的具有挑战性的代码生成数据集上测试了AlphaCodium，该数据集包含来自Codeforces等平台的竞赛编程问题。所提出的流程始终且显著地提升了结果。例如，在验证集上，GPT-4的准确率（pass@5）从单一精心设计的直接提示的19%提升至AlphaCodium流程的44%。我们相信，本工作中获得的许多原则和最佳实践广泛适用于一般的代码生成任务。完整实现见：https://github.com/Codium-ai/AlphaCodium