AMBER: Age-Aware Memory Evolution for Continual Adaptation of Large Language Models

Continual fine-tuning adapts a large language model to a sequence of tasks. However, fitting a new task tends to overwrite knowledge from earlier tasks whose data is no longer available. Experience replay addresses this difficulty by retaining a small buffer of historical samples and training them along with the current task, but existing methods decide separately which samples enter the buffer and how often each is replayed. Because the buffer has a fixed capacity and is largely frozen once written, adaptation reduces to adjusting the replay frequency, and the stored content cannot keep pace as the gradient relationships between earlier and current tasks drift during training. To tackle this, we propose AMBER, a buffer management framework that manages the Age-aware Memory Buffer with Evolving Replay. The age is defined as the temporal distance between a sample and the current task. Using task temporal distance to encode each sample's historical position and its conflict with the current task, AMBER jointly regulates buffer writing, maintenance, and sampling for dynamic memory evolution. Writing draws candidates from a reservoir stream, a loss-quantile band, and slow-adapter generation. Maintenance replaces unprotected entries with generated traces at an age-dependent rate while protected anchors retain a real-data backbone. Sampling weights replay by age. On an eight-task sequence with Llama-2-7B, AMBER improves overall performance by 21.1% over the dual-adapter baseline and is the only compared method that does not degrade backward transfer. These results indicate that one interpretable age signal can govern buffer composition and replay scheduling together within a fixed storage budget, improving the stability and plasticity trade-off that continual learning must balance.