Rotation-Based Gradient Balancing: Reconciling Alignment and Fidelity in Multi-Task Learning

Multi-task learning (MTL) enables knowledge sharing across tasks but often suffers from gradient conflicts. Gradient manipulation methods reduce these conflicts by directly altering task gradient angles to eliminate opposing directions. However, over-prioritizing geometric alignment may introduce substantial deviations from the original gradient directions, diluting valuable task-specific information and degrading performance. To overcome these limitations, we propose the Rotation-Based Gradient Balancing (RGB) method. RGB enables task gradients to be finely adjusted toward a consensus direction, holistically reducing global conflict while penalizing excessive deviation from the original task-gradient directions. Our method consistently achieves competitive results across various MTL benchmarks ranging from 3 to 40 tasks. Empirically, ablation studies show that simply increasing the degree of rotation does not consistently improve task performance, reinforcing the importance of controlling gradient modification when balancing alignment and fidelity.