Kuang Yu

Reinforcement learning amortizes transition-state physics into one-step flow models

Tefle is introduced, a one-step flow model that amortizes physics-guided generation into training: a mean-flow objective replaces multi-step sampling, a low-cost surrogate replaces density functional theory (DFT) reward evaluations, and reinforcement-learning post-training with rewards that enforce those conditions rep...