  A modular design encourages neural models to disentangle and recombine
different facets of knowledge to generalise more systematically to new tasks.
In this work, we assume that each task is associated with a subset of latent
discrete skills from a (potentially small) inventory. In turn, skills
correspond to parameter-efficient (sparse / low-rank) model parameterisations.
By jointly learning these and a task-skill allocation matrix, the network for
each task is instantiated as the average of the parameters of active skills. To
favour non-trivial soft partitions of skills across tasks, we experiment with a
series of inductive biases, such as an Indian Buffet Process prior and a
two-speed learning rate. We evaluate our latent-skill model on two main
settings: 1) multitask reinforcement learning for grounded instruction
following on 8 levels of the BabyAI platform; and 2) few-shot adaptation of
pre-trained text-to-text generative models on CrossFit, a benchmark comprising
160 NLP tasks. We find that the modular design of a network significantly
increases sample efficiency in reinforcement learning and few-shot
generalisation in supervised learning, compared to baselines with fully shared,
task-specific, or conditionally generated parameters where knowledge is
entangled across tasks. In addition, we show how discrete skills help
interpretability, as they yield an explicit hierarchy of tasks.
