First-Order MAML

On First-Order Meta-Learning Algorithms

OpenAI published research on first-order meta-learning algorithms, presenting simplified variants of MAML (Model-Agnostic Meta-Learning) that omit second-order derivatives while retaining competitive performance. The work demonstrates that first-order approximations are surprisingly effective for few-shot learning tasks. This contributed to the broader understanding of gradient-based meta-learning efficiency and scalability.