Reptile: Scalable meta-learning algorithm using SGD/Adam, similar to first-order MAML

AI Impact Summary

Reptile applies the Shortest Descent approach to meta-learning by repeatedly sampling a task, performing SGD/Adam steps on that task, and updating the meta-parameters toward the task-end state. It is mathematically similar to first-order MAML and relies on black-box optimizers, reducing implementation complexity and potential compute relative to second-order methods. For a production team, this enables easier integration into existing ML pipelines (e.g., PyTorch, TensorFlow) for scalable few-shot learning, but requires careful tuning of task sampling and optimizer hyperparameters to ensure stable convergence across diverse task distributions. Plan benchmarking against MAML baselines, and establish task data handling, checkpointing, and monitoring to quantify gains in adaptation speed and compute efficiency.