Adam ATAN2 Pytorch: PyTorch Optimizer

Understand the project

Practical overview

Use it when you want to try the Adam-atan2 update rule in a PyTorch training loop, including experiments focused on continual-learning plasticity.

PyTorch developers evaluating alternative optimizers for neural-network training.
Researchers experimenting with plasticity in continual-learning setups.

Install an alternative optimizer package into a Python environment.
Pass PyTorch model parameters to AdamAtan2 within a training loop.
Experiment with an atan2-based update rule and continual-learning plasticity features.

Before you choose

The repository is public and MIT-licensed. The README documents installation through pip.

It is a library for integration into Python/PyTorch code, not a hosted model-training service.
The README’s example requires the user to supply a PyTorch model, loss computation and training loop.
The project describes intended properties of its update rule but the supplied README does not report comparative benchmarks.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Add the optimizer to a model training loop and perform optimizer steps.

Install the package with the command below.
Documented command
```
pip install adam-atan2-pytorch
```
Import AdamAtan2 from adam_atan2_pytorch and instantiate it with model.parameters() and a learning rate.
For each training iteration, compute a loss and call loss.backward().
Call opt.step() to update parameters, then opt.zero_grad() to clear gradients.

The training loop uses AdamAtan2 to update the supplied model parameters.

The README’s example uses a toy linear model and synthetic inputs; it does not provide benchmark results or task-specific training guidance.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 8 October 2026
Original website title: lucidrains/adam-atan2-pytorch · Official homepage title · retained in its source language