Replay on Demand: An Emergent Curriculum for Balancing Adaptation and Forgetting in Continued Pretraining
This study addresses the challenge of balancing new domain adaptation against prior knowledge forgetting during continual pre-training by proposing an on-demand replay mechanism. Without requiring predefined data mixing ratios, this method dynamically allocates a shared training budget through learning potential assessment and forgetting monitoring. Furthermore, it constructs an online curriculum via a competitive generation strategy to achieve adaptive data replay and model state optimization. Experimental results demonstrate that the proposed mechanism significantly outperforms fixed-replay baselines across multi-scale models while effectively mitigating catastrophic forgetting. Ultimately, this work establishes a novel paradigm for resource-efficient utilization in continual learning.