Measuring and Harnessing Transference in Multi-Task Learning
This work addresses the problem of optimizing task selection and gradient combination in multi-task learning for researchers and practitioners, offering incremental improvements over prior methods.
The paper tackled the challenge of identifying beneficial tasks for co-training in multi-task learning by analyzing information transfer dynamics, resulting in a transference metric that improved performance on four benchmarks.
Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naive formulations often degrade performance and in particular, identifying the tasks that would benefit from co-training remains a challenging design question. In this paper, we analyze the dynamics of information transfer, or transference, across tasks throughout training. Specifically, we develop a similarity measure that can quantify transference among tasks and use this quantity to both better understand the optimization dynamics of multi-task learning as well as improve overall learning performance. In the latter case, we propose two methods to leverage our transference metric. The first operates at a macro-level by selecting which tasks should train together while the second functions at a micro-level by determining how to combine task gradients at each training step. We find these methods can lead to significant improvement over prior work on three supervised multi-task learning benchmarks and one multi-task reinforcement learning paradigm.