This repository provides a PyTorch implementation of the gradient surgery technique for multi-task learning. It wraps standard PyTorch optimizers with a PCGrad class that modifies conflicting gradients between tasks before applying updates. The implementation supports multi-task scenarios including Multi-MNIST and is designed to prevent gradient interference when training a single model on multiple objectives.