Provided a curriculum where constituent objectives are strongly correlated with the final objective, corresponding gradients from sequential simpler tasks are more effective at traversing the optimization landscape and can accelerate convergence. Similarly, CL can be applied to non-gradient based objectives, e.g., presence of a target structural motif, by devising a curriculum with gradually increasing complexity, e.g., decomposing the target structural motif into simpler constituents. Molecular design often requires optimizing correlated properties that cumulatively define favourable chemical space, e.g., generating known active scaffolds and improving binding affinity. By applying concepts from CL, existing limitations of policy-based RL for molecular design can be circumvented. CL provides a strategy to lower the learning barrier of complex MPO objectives, reaching a state of productivity within a reasonable timeframe.