Yogi Optimizer [better] Guide

: Yogi dynamically adjusts the learning rate based on historical gradient information. It reduces the rate when gradients are noisy and increases it when they are stable, enhancing both efficiency and stability. Empirical Benefits and Use Cases

While Adam adds the new information, Yogi chooses to model the update differently. The Yogi algorithm modifies the update rule to focus on how the sign of the current gradient interacts with the accumulated statistics. yogi optimizer

Yogi is available in optax , the standard optimization library for JAX. : Yogi dynamically adjusts the learning rate based

While newer optimizers like (focuses on belief in observed gradients) and Lamb (layer-wise adaptation) have since emerged, Yogi remains the gold standard for scenarios where gradient variance is high and spurious. The Yogi algorithm modifies the update rule to

Where $g_t$ is the current gradient. If you unroll this, $v_t$ is essentially an of squared gradients.