OpenAI Codex was trained on carefully curated datasets sourced primarily from publicly available code repositories, with the largest portion coming from GitHub. The original 2021 version of Codex used approximately 159 gigabytes of Python code extracted from over 54 million GitHub repositories, making Python the most heavily represented language in the training data. Beyond Python, the training dataset included substantial amounts of code in JavaScript, TypeScript, Go, Ruby, C++, C#, Java, PHP, Swift, and many other programming languages. The selection process focused on repositories with indicators of code quality such as stars, forks, and active maintenance, while filtering out repositories that appeared to contain auto-generated code, duplicates, or low-quality implementations.
The training data extended beyond just source code files to include related technical content that helps with understanding programming context. This included README files, documentation, code comments, issue discussions, and other text that provides context about how code is used and what problems it solves. The dataset also incorporated various types of software projects ranging from simple utility scripts to complex enterprise applications, web frameworks, data science libraries, mobile applications, and system-level software. This diversity ensures that Codex learned patterns from different programming paradigms including object-oriented programming, functional programming, and procedural programming across various domains like web development, machine learning, systems programming, and mobile development.
The current version of Codex, built on the codex-1 model using OpenAI’s o3 architecture, likely incorporates an even more extensive and refined dataset, though specific details haven’t been publicly disclosed. The training methodology has evolved to include reinforcement learning from human feedback (RLHF), which means the model was also trained on examples of human developer workflows, code review processes, and iterative development practices. This additional training data helps explain why the current Codex can handle complete software engineering tasks rather than just generating code snippets. The training process also included safety filtering to remove potentially harmful code, licensing considerations to respect intellectual property, and quality controls to ensure the model learned from high-quality examples that represent best practices in software development.