Oracle-Guided Fine-Tuning

A fine-tuning method that uses reference text (oracle information) available during training as a supervision signal. By constructing correction, regularization, and hard-negative sample pools, it trains a model to learn a hybrid policy of selection and rewriting, and is applied mainly to generative ASR post-editing.