Thomas T. Zhang

Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning

A long-standing goal of AI is a model that can continually learn and improve itself. On post-trained models, supervised finetuning (SFT) on new data often causes poor generalization and catastrophic forgetting. As such, the conventional wisdom is that on-policy training is a prerequisite for continual learning. In prac...