CAGrad
CAGrad introduces a novel approach to multitask learning by utilizing conflict-averse gradient descent, which optimizes multiple objectives simultaneously. Recognized at NeurIPS 2021, this methodology reduces the computational burden in calculating task gradients, enhancing efficiency for varied applications. With the addition of FAMO, the tool further supports dynamic optimization without computing all task gradients. Experiments on NYU-v2, CityScapes, and Metaworld datasets illustrate its effectiveness in image-to-image prediction and reinforcement learning. This resource aids researchers in optimizing multitask objectives with minimal resource usage.