Task Grouping

Task grouping in multi-task learning (MTL) aims to optimize the performance of models trained on multiple, potentially related tasks by strategically grouping similar tasks for joint training. Current research focuses on developing automated methods for identifying optimal task groupings, often employing differentiable pruning techniques or anomaly detection to assess task compatibility, and utilizing novel architectures like branched networks to manage inter-task relationships and mitigate negative transfer. Effective task grouping improves training efficiency, enhances model accuracy and fairness, and is particularly relevant for large-scale applications like reinforcement learning in network optimization and multilingual text recognition where managing numerous tasks is crucial.