# Learning to Multi-Task by Active Sampling

arXiv: https://arxiv.org/abs/1702.06053 · HTML: https://mlsanity.com/paper/1702.06053/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data](https://mlsanity.com/paper/2302.00674/) (2023)
- [Learning Curricula in Open-Ended Worlds](https://mlsanity.com/paper/2312.03126/) (2023)
- [PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent](https://mlsanity.com/paper/2202.00531/) (2022)
- [Auxiliary Loss Reweighting for Image Inpainting](https://mlsanity.com/paper/2111.07279/) (2021)
- [All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass](https://mlsanity.com/paper/2205.10744/) (2022)

## Abstract (arXiv)

One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training. In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision. Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones. We propose three distinct models under our active sampling framework. An adaptive method with extremely competitive multi-tasking performance. A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem. A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor critic methods for optimizing the multi-tasking performance directly. We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.