We propose an offline guarded safe RL framework for medical treatment, providing theoretical safety guarantees and improving both clinical consistency and treatment outcomes.

We propose a method for learning energy-preserving or -dissipating numerical schemes. Our approach admits the universal approximation property.

We propose a new benchmark that tasks LMs with writing efficient code for widely used math, science and computer science functions.