Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization
This work introduces Elite-Weighted Supervised Fine-tuning (EW-SFT), which uses reward to guide elite selection of high-scoring molecules, and updates the model by its own pretraining loss on that set, and consistently outperforms the corresponding native optimizers.
Shiyun Wa, Yifei Wang, A. G. Green et al.
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