Fantastic Multi-Task Gradient Updates and How to Find Them In a Cone

• URL: http://arxiv.org/abs/2502.00217v1
• Date: Fri, 31 Jan 2025 23:11:12 GMT
• Title: Fantastic Multi-Task Gradient Updates and How to Find Them In a Cone
• Authors: Negar Hassanpour, Muhammad Kamran Janjua, Kunlin Zhang, Sepehr Lavasani, Xiaowen Zhang, Chunhua Zhou, Chao Gao,
• Abstract summary: We propose ConicGrad, a principled, scalable, and robust MTL approach formulated as a constrained optimization problem. Our method introduces an angular constraint to dynamically regulate gradient update directions, confining them within a cone centered on the reference gradient of the overall objective. We conduct extensive experiments on standard supervised learning and reinforcement learning MTL benchmarks, and demonstrate that ConicGrad achieves state-of-the-art performance across diverse tasks.
• Score: 14.702480423653984
• License:
• Abstract: Balancing competing objectives remains a fundamental challenge in multi-task learning (MTL), primarily due to conflicting gradients across individual tasks. A common solution relies on computing a dynamic gradient update vector that balances competing tasks as optimization progresses. Building on this idea, we propose ConicGrad, a principled, scalable, and robust MTL approach formulated as a constrained optimization problem. Our method introduces an angular constraint to dynamically regulate gradient update directions, confining them within a cone centered on the reference gradient of the overall objective. By balancing task-specific gradients without over-constraining their direction or magnitude, ConicGrad effectively resolves inter-task gradient conflicts. Moreover, our framework ensures computational efficiency and scalability to high-dimensional parameter spaces. We conduct extensive experiments on standard supervised learning and reinforcement learning MTL benchmarks, and demonstrate that ConicGrad achieves state-of-the-art performance across diverse tasks.

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