This repository provides the code and experimental setup for the "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets" paper. It allows researchers and practitioners to reproduce and extend experiments on the grokking phenomenon, where models generalize unexpectedly after a period of overfitting.

How It Works

The project implements training procedures for small algorithmic datasets, focusing on the "grokking" effect. It likely utilizes standard deep learning frameworks and techniques to train models, observing their generalization behavior over extended training periods. The core novelty lies in the experimental setup designed to isolate and study this specific generalization phenomenon.

Quick Start & Requirements

• Primary install / run command:

  pip install -e .
  ./scripts/train.py
  

• Prerequisites: Python, standard deep learning libraries (likely PyTorch or TensorFlow, though not explicitly stated).

Highlighted Details

• Codebase for the "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets" paper.
• Focuses on studying the grokking phenomenon in deep learning models.

Maintenance & Community

• Developed by OpenAI researchers. No community links or roadmap are provided in the README.

Licensing & Compatibility

• The license is not specified in the README.

Limitations & Caveats

The README is extremely minimal, lacking details on specific dependencies, hardware requirements (e.g., GPU, CUDA), dataset availability, or the exact model architectures used. The scope appears limited to reproducing the paper's experiments.