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*learned\_optimization* is a research codebase for training, designing, evaluating, and applying learned
optimizers, and for meta-training of dynamical systems more broadly. It implements hand-designed and learned optimizers, tasks to meta-train and meta-test them, and outer-training algorithms such as ES, PES, and truncated backprop through time.

To get started see our [documentation](https://learned-optimization.readthedocs.io/en/latest/).
There is a third-party (alpha) [PyTorch wrapper for VeLO](https://github.com/janEbert/PyTorch-VeLO) (the current SOTA learned optimizer in this repository) by Jan Ebert.

## Quick Start Colab Notebooks
Our [documentation](https://learned-optimization.readthedocs.io/en/latest/) can also be run as colab notebooks! We recommend running these notebooks with a free accelerator (TPU or GPU) in colab (go to `Runtime` -> `Change runtime type`).
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