Efficient machine learning (ML) has become increasingly important as models grow larger and data volumes expand. In this work, we address the trade-off between generalization in multi-task learning (MTL) and precision in single-task learning (STL) by introducing the Multi-Task to Single-Task (MT2ST) framework. MT2ST is designed to enhance training efficiency and accuracy in multi-modal tasks, showcasing its value as a practical application of efficient ML.
@article{arxiv.2406.18038,
title = {MT2ST: Adaptive Multi-Task to Single-Task Learning},
author = {Dong Liu and Yanxuan Yu},
journal= {arXiv preprint arXiv:2406.18038},
year = {2025}
}