Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models

Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida +2

While foundation models have been exploited for various expert tasks through fine-tuning, any foundation model will become outdated due to its old knowledge or limited capability.…