Yogi Optimizer -

Yogi adds a tiny bit of compute per step and may need slightly more memory. In practice, it's negligible for most models.

Try it on your next unstable training run. You might be surprised. 🚀 yogi optimizer

Most deep learning practitioners reach for Adam by default. But when training on tasks with noisy or sparse gradients (like GANs, reinforcement learning, or large-scale language models), Adam can sometimes struggle with sudden large gradient updates that destabilize training. Yogi adds a tiny bit of compute per

Enter (You Only Gradient Once).

Developed by researchers at Google and Stanford, Yogi modifies Adam's adaptive learning rate mechanism to make it more robust to noisy gradients. or large-scale language models)