Constrained decision-making problems are pervasive across various disciplines.

Continuous allocation tasks are a class of problems where an agent needs to distribute a limited amount of resources over a set of entities at each time step.

We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework.

This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constraints serve as conditioning variables.