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About this paper
Edoardo M. Ponti, Alessandro Sordoni, Yoshua Bengio, Siva Reddy
A latent-skill model called SKILLED learns discrete, reusable skills and their allocation to tasks in a multitask learning setting. This approach improves sample efficiency, systematic generalization to unseen tasks, and offers interpretability by revealing task hierarchies.
Key insights
- Explicitly disentangling knowledge into reusable, discrete skills provides a stronger inductive bias for systematic generalization and efficient knowledge transfer across tasks.
- Models can automatically discover more effective task synergies and skill compositions than those based on human expert-defined categories, leading to improved performance.
- Learning a sparse, discrete task-skill allocation matrix enhances interpretability by revealing an explicit hierarchy of tasks and their shared underlying knowledge modules.
Summary by alphaXiv.
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