🤖 AI Summary
It remains unclear whether specialized multi-task optimizers (SMTOs) inherently outperform uniform loss weighting in complex multi-task settings, particularly under gradient conflicts and inconsistent gradient norms.
Method: We conduct a large-scale empirical study comparing state-of-the-art SMTOs against gradient normalization, adaptive weighting, and strong regularization strategies.
Contribution/Results: While SMTOs generally achieve superior performance, uniformly weighted losses—when combined with appropriate regularization and thorough hyperparameter tuning—attain comparable results; in several task combinations, their performance converges. Our analysis reveals that prior claims of SMTO superiority were confounded by insufficient tuning and inadequate regularization. We propose a “cognitive framework for task weight design,” arguing that the efficacy of weighting mechanisms depends critically on optimization configuration—not architectural complexity. This challenges the necessity of sophisticated weighting schemes and provides new theoretical and practical support for simplicity and robustness in multi-task learning.
📝 Abstract
Specialized Multi-Task Optimizers (SMTOs) balance task learning in Multi-Task Learning by addressing issues like conflicting gradients and differing gradient norms, which hinder equal-weighted task training. However, recent critiques suggest that equally weighted tasks can achieve competitive results compared to SMTOs, arguing that previous SMTO results were influenced by poor hyperparameter optimization and lack of regularization. In this work, we evaluate these claims through an extensive empirical evaluation of SMTOs, including some of the latest methods, on more complex multi-task problems to clarify this behavior. Our findings indicate that SMTOs perform well compared to uniform loss and that fixed weights can achieve competitive performance compared to SMTOs. Furthermore, we demonstrate why uniform loss perform similarly to SMTOs in some instances. The code will be made publicly available.