Teacher–student curriculum learning

OpenAI has published a blog post detailing a training approach called teacher–student curriculum learning. In this method, a larger, more capable teacher model guides a smaller student model through a sequence of tasks that increase in difficulty. The curriculum is dynamically adjusted based on the student's current performance, enabling efficient knowledge transfer. According to OpenAI Blog, this technique allows the student model to achieve stronger performance on target tasks compared to static training methods. For developers building AI workflows, this means they can potentially train more capable smaller models for deployment, reducing computational costs while maintaining high accuracy. The approach is especially relevant for fine-tuning models on niche domains or resource-constrained environments.