Uncertainty weighting is a prominent method used in multitask machine learning. Multitask learning inherently entails a multi-objective nature due to potential conflicts between distinct tasks. Tuning these weights by hand proves to be difficult and resource-intensive, making multi-task learning prohibitive in practice. In particular, this concept resembles the objective loss function outlined in Equation . Therefore, we are evaluating the efficacy of this method in optimizing the CG model. The uncertainty weighting learns to numerically balance a linear combination of loss functions tied to individual tasks, considering the homoscedastic uncertainty of each task. The multi-task learning/optimization is treated as a probabilistic model, and a set of learnable parameters is used to signify the task-specific uncertainty. The combination of multi-objective loss functions can be written as: