  Meta-learning is a tool that allows us to build sample-efficient learning
systems. Here we show that, once meta-trained, LSTM Meta-Learners aren't just
faster learners than their sample-inefficient deep learning (DL) and
reinforcement learning (RL) brethren, but that they actually pursue
fundamentally different learning trajectories. We study their learning dynamics
on three sets of structured tasks for which the corresponding learning dynamics
of DL and RL systems have been previously described: linear regression (Saxe et
al., 2013), nonlinear regression (Rahaman et al., 2018; Xu et al., 2018), and
contextual bandits (Schaul et al., 2019). In each case, while
sample-inefficient DL and RL Learners uncover the task structure in a staggered
manner, meta-trained LSTM Meta-Learners uncover almost all task structure
concurrently, congruent with the patterns expected from Bayes-optimal inference
algorithms. This has implications for research areas wherever the learning
behaviour itself is of interest, such as safety, curriculum design, and
human-in-the-loop machine learning.
