In-Loop Meta-Learning with Gradient-Alignment Reward

Understand

At the heart of the standard deep learning training loop is a greedy gradient step minimizing a given loss.

• We propose to add a second step to maximize training generalization.
• To do this, we optimize the loss of the next training step.
• While computing the gradient for this generally is very expensive and many interesting applications consider non-differentiable parameters (e.g.