GitHub Introduces Custom Model to Enhance Copilot’s Code Completion
GitHub has introduced a new custom model to enhance its AI-powered coding assistant, Copilot, focusing on improving code completion speed and accuracy based on developer feedback.
The updates to GitHub Copilot aim to deliver more relevant and efficient code suggestions. These improvements include a 20% increase in accepted and retained characters, a 12% higher acceptance rate, and a threefold increase in token-per-second throughput, along with a 35% reduction in latency. These changes are designed to improve the overall experience across various editors and environments, allowing developers to spend less time editing and more time building.
By optimizing for accepted and retained characters and code flow, GitHub seeks to provide suggestions that developers find more useful and relevant, ultimately enhancing productivity. The updated model ensures that a greater portion of Copilot's suggestions remain in the final code, reducing unnecessary keystrokes.
To ensure the effectiveness of the new model, GitHub employed a multi-layered evaluation strategy, including offline, pre-production, and production evaluations, each contributing to refining different aspects of the code completion experience. The model's performance is assessed through metrics like accepted-and-retained characters, acceptance rates, and latency, ensuring real-world applicability and developer satisfaction.
The training process for the new model involved mid-training on a curated corpus of modern code, followed by supervised fine-tuning and reinforcement learning. This approach ensured the model's fluency, consistency in style, and awareness of context. The reinforcement learning algorithm focused on enhancing code quality, relevance, and helpfulness, resulting in completions that are more precise and useful for developers.
Looking ahead, GitHub plans to expand Copilot's capabilities into domain-specific areas such as game engines and financial systems. The team is also working on refining reward functions to further improve the quality and relevance of code completions, ensuring that Copilot continues to offer high-quality assistance in diverse developer environments.
The enhancements to GitHub Copilot underscore the platform's commitment to leveraging AI to improve developer productivity and streamline the coding process. By integrating developer feedback and focusing on real-world application, GitHub aims to offer a more intuitive and effective coding assistant.