arXiv:2509.16959v5 Announce Type: replace Abstract: When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, thereby potentially reducing the final model's performance. To address this, we introduce SON-GOKU, a scheduler that computes gradient interference, constructs an interference graph, and then applies greedy graph-coloring to partition tasks into groups that align well with each other. At each training step, only one group (color class) of tasks are activated, and the grouping partition is constantly recomputed as task
The increasing complexity of multi-task learning models and the computational costs associated with them are driving the need for more efficient optimization techniques.
Improving multi-task learning efficiency directly impacts the development of more capable and resource-optimized AI systems across various applications.
The proposed SON-GOKU scheduler offers a novel approach to mitigate gradient interference, potentially leading to faster convergence and better performance in complex multi-task AI models.
- · AI model developers
- · Cloud computing providers (from increased training efficiency)
- · SaaS companies leveraging multi-task AI
- · Research institutions
- · Inefficient multi-task learning optimization methods
- · Hardware providers if efficiency gains reduce compute demand (though unlikely at
More robust and performant multi-task AI models become achievable with less computational overhead.
Accelerated development of AI agents capable of handling diverse and conflicting objectives.
Deeper integration of complex AI systems into white-collar workflows, as agentic capabilities improve and training costs decrease.
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