Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning
arXiv:2604.07848v2 Announce Type: replace Abstract: Multi-task learning shows strikingly inconsistent results -- sometimes joint training helps substantially, sometimes it actively harms performance -- yet the field lacks a principled framework for predicting these outcomes. We identify a fundamental but unstated assumption underlying gradient-based task analysis: tasks must share training instances for gradient conflicts to reveal genuine relationships. When tasks are measured on the same inputs, gradient alignment reflects shared mechanistic structure; when measured on disjoint inputs, any a
The rapid advancement and widespread application of multi-task learning in AI necessitate a deeper theoretical understanding to optimize its performance and deployment.
This research provides a principled framework for understanding and predicting the efficacy of multi-task learning, which is crucial for developing robust and efficient AI systems.
The ability to better assess task relationships will lead to more effective multi-task model design, reducing wasted computational resources and improving model reliability.
- · AI researchers
- · AI developers
- · Companies deploying generalized AI systems
- · AI projects with poorly designed multi-task architectures
- · Organizations relying on brute-force multi-task learning approaches
Improved understanding of multi-task learning will lead to more effective model architectures.
More reliable and efficient multi-task AI models will accelerate the development of generalized AI agents.
The enhanced capability of generalized AI agents could further collapse white-collar workflows across various industries.
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Read at arXiv cs.LG