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# Reptile: A scalable meta-learning algorithm

**[OpenAI](https://daily.dev/sources/openai)** · 4 min read · 0 upvotes · 0 comments

## Summary

OpenAI introduces Reptile, a scalable meta-learning algorithm that finds a good neural network initialization for fast adaptation to new tasks. Unlike MAML, Reptile avoids unrolling computation graphs or computing second derivatives — it simply runs SGD on sampled tasks and updates initial parameters toward the learned parameters. This makes it more memory- and compute-efficient than MAML while achieving comparable performance on few-shot classification benchmarks (Omniglot, Mini-ImageNet). A Taylor series analysis shows Reptile maximizes inner products between gradients of different minibatches, explaining its generalization properties. The implementation is available in TensorFlow and PyTorch on GitHub.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://openai.com/index/reptile>

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