Sangwon Cho

Quality-Aware Selection for Retrieval-Augmented Fine-Tuning of Small Language Models

Quality-aware selection is a promising, data-efficient safeguard for synthetic RAFT data—performing comparably to full-pool training at one third of the cost, with growing value as pool quality degrades—and larger-scale external validation remains future work.