Finetuning with sampling demonstrates strong generalization in models
The study examines post-training methods, challenging the assumption that supervised finetuning is prone to catastrophic forgetting and weak generalization
The study examines post-training methods, challenging the assumption that supervised finetuning is prone to catastrophic forgetting and weak generalization