Learning to Select Source Domains: Proxy-Rewarded Policy Optimization for Molecular OOD Generalization
This work addresses poor prediction of molecular properties when models encounter unseen chemical structures (OOD). It proposes a framework called POMA that uses policy optimization to intelligently choose the best set of source data for knowledge transfer. By selecting optimal sources based on proxy rewards, the method prevents negative transfer and significantly improves accuracy on challenging, novel molecules.