Reptile: A scalable meta-learning algorithm
OpenAI just published a meta-learning algorithm that matches MAML performance with simpler math.
Why it matters
Reptile demonstrates a more computationally efficient approach to meta-learning that could lower the barrier for training models that adapt quickly to new tasks—relevant for anyone building few-shot or transfer learning systems.
The key facts
10 to knowAlgorithm: Reptile (meta-learning via task sampling + SGD)
Mathematical relationship: First-order MAML equivalent with black-box optimizer access
Performance: Similar to MAML with reduced computational overhead
Published: OpenAI research, March 2018
Use case: Few-shot learning and task adaptation
Mathematical relation: First-order MAML equivalent
Key advantage: Black-box optimizer access (works with SGD/Adam)
Performance parity with MAML at lower computational cost
Published by OpenAI, March 2018
APPLICATION: Few-shot learning, task adaptation
Go to the source
OpenAI Blogopenai.com
Publisher excerpt: We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task. Reptile is the application of the Shortest Descent…