Humans develop learning algorithms that are incredibly general and can be applied across a wide range of tasks. Unfortunately, this process is often tedious trial and error with numerous possibilities for suboptimal choices. General meta-learning seeks to automate many of these choices, generating new learning algorithms automatically. Different from contemporary meta-learning, where the generalization ability has been limited, these learning algorithms ought to be general-purpose. This allows us to leverage data at scale for learning algorithm design that is difficult for humans to consider. I present a General Meta Learner, MetaGenRL, that meta-learns novel Reinforcement Learning algorithms that can be applied to significantly different environments. We further investigate how we can reduce inductive biases and simplify meta-learning. Finally, I introduce variable-shared meta-learning (VS-ML), a novel principle that generalizes learned learning rules, fast weights, and meta-RNNs (learning in activations). This enables (1) implementing backpropagation purely in the recurrent dynamics of an RNN and (2) meta-learning algorithms for supervised learning from scratch.