Efficient training of language models to fill in the middle

OpenAI has published research on a training method for language models that focuses on generating text in the middle of a prompt rather than only left-to-right. This technique, called fill-in-the-middle (FIM), is especially relevant for code completion where infilling—like completing a function body between its signature and closing brace—is a common task. The paper demonstrates that models trained with a FIM objective can be both more sample-efficient and performant on infilling tasks compared to standard causal language models. Notably, the work examines scaling laws for FIM training, showing that performance gains persist at larger model sizes. For developers building AI-powered coding workflows, this means that future iterations of code completion tools like GitHub Copilot could become more accurate and require less training data to achieve strong infilling capabilities. The research also opens up possibilities for other applications where generating the middle of a sequence is valuable, such as text editing or document insertion.