Title: Preserving Gradient Harmony: A Rotation-Based Gradient Balancing for Multi-Task Conflict Remedy
Primary area: optimization
Keywords: Multi-task Learning, Optimization, Deep Learning, Gradient Conflicts, Pareto Optimality

Abstract: Multi-task learning (MTL) enables knowledge sharing across tasks but often suffers from gradient conflicts, leading to performance imbalances among tasks. Existing weighting-based methods attempt to balance the directional conflicts by striving for the optimal weights computed from gradient or loss information. However, those indirect weighting operations face a limited balancing effect, as the gradient's per-dimensional sensitivities are omitted. Alternatively, gradient manipulation methods such as PCGrad, GradDrop, etc., directly control the task gradients to eliminate opposing gradient directions, but their over-aggressive operations potentially harm the gradient properties, leading to suboptimal updates. They are associated with the issues of over-correction, order dependence, and poor scalability in high-dimensional task settings. To overcome these limitations, we propose the Rotation-Based Gradient Balancing (RGB), a novel algorithm that rotates normalized task gradients toward a consensus direction using independently optimized per-task angle corrections. Unlike projections, rotations provide fine-grained control that preserves beneficial gradient components, reduces global conflicts holistically, and implicitly incorporates loss change information for balanced optimization. Empirical results demonstrate the effectiveness and consistency of RGB, achieving state-of-the-art performance in various datasets, where RGB is the first method on the QM9 dataset with 11 tasks to surpass single-task baselines on average, and its performance is consistent across various benchmarks ranging from 3–40 tasks. Moreover, we propose the concept of multi-task equilibrium relationship that is supported by our empirical experiment and inferring the phenomenon of miss-correction angular error. We also provide the theoretical global convergence of RGB to Pareto stationary under standard smoothness assumptions.

TL;DR: Gradient manipulation methods often over-correct task gradients; we introduce RGB to balance multi-task conflicts by optimally rotating them toward consensus, yielding state-of-the-art results.