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Meta-learning strives to learn about and improve a student's machine learning algorithm. However, existing meta-learning methods either only work with differentiable algorithms or are hand-crafted to improve one specific component of an algorithm. We develop a unifying meta-learning framework, called Reinforcement Teaching, to improve the learning process of any algorithm. Under Reinforcement Teaching, a teaching policy is learned, through reinforcement, to improve a student's learning. ToarXiv:2204.11897v2 fatcat:c2226jnpmfhvvjh25d3jyuseqy