openai/supervised-reptile

• highreadme#1

  Reposition README's opening to clarify purpose and audience

  Why:

  CURRENT

  **Status:** Archive (code is provided as-is, no updates expected)
  
  # supervised-reptile
  
  Reptile training code for Omniglot and Mini-ImageNet.

  COPY-PASTE FIX

  Copy

  **Status:** Archive (code is provided as-is, no updates expected)
  
  # supervised-reptile: Reference Code for First-Order Meta-Learning (Reptile)
  
  This repository contains the original training code for the Reptile meta-learning algorithm, specifically for Omniglot and Mini-ImageNet datasets. It serves as a direct implementation of the methods described in the paper "On First-Order Meta-Learning Algorithms," primarily for researchers and practitioners interested in meta-learning and few-shot adaptation.

  OpenDoneDismiss
• hightopics#2

  Add specific meta-learning and dataset topics

  Why:

  CURRENT

  paper

  COPY-PASTE FIX

  Copy

  paper, meta-learning, few-shot-learning, reptile-algorithm, omniglot, mini-imagenet, machine-learning, deep-learning

  OpenDoneDismiss
• mediumreadme#3

  Add a brief comparison or context section for Reptile

  Why:

  COPY-PASTE FIX

  Copy

  ## Reptile in Context
  
  Reptile is a first-order meta-learning algorithm that aims to find a good model initialization for rapid adaptation to new tasks. Unlike some other meta-learning methods (e.g., MAML), Reptile uses a simpler, first-order update rule, making it computationally efficient while still achieving strong performance in few-shot learning scenarios.

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to find a better model initialization for faster adaptation to new tasks?

  you: not recommended
  AI recommended (in order):

  1. Hugging Face Transformers Library
  2. PyTorch MAML (Model-Agnostic Meta-Learning) Implementations (learn2learn/learn2learn)
  3. TensorFlow Meta-Dataset
  4. OpenAI's GPT-3.5/GPT-4 API
  5. Fast.ai Library
  6. Keras Applications

  AI recommended 6 alternatives but never named openai/supervised-reptile. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Seeking code examples for meta-learning algorithms on Omniglot or Mini-ImageNet datasets.

  you: not recommended
  AI recommended (in order):

  1. Learn2Learn
  2. Meta-Learning with PyTorch
  3. Higher
  4. TorchMeta
  5. TensorFlow Meta-Learning
  6. DeepMind's MAML implementation

  AI recommended 6 alternatives but never named openai/supervised-reptile. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of openai/supervised-reptile?

  pass
  ⚠AI did not name openai/supervised-reptile — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts openai/supervised-reptile in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named openai/supervised-reptile explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo openai/supervised-reptile solve, and who is the primary audience?

  pass
  ✓AI named openai/supervised-reptile explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?