Meta-learning stands out as a potent strategy to facilitate the rapid acquisition of new skills by AI systems, even with limited data. This methodology fosters the exploration of representations and learning approaches that can extend to unfamiliar tasks. Consequently, the construction of task distributions with ample breadth becomes imperative, ensuring that meta-learning models are exposed