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The core mechanism of experience replay is to maintain a memory buffer of limited capacity that stores some samples from historical tasks, and when training on a new task, mix them with the current data to form mini-batches, so that the loss function always contains gradient signals from the old distribution.
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10/27/2026
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UnverifiedThe fundamental limitation of replay strategies is the stability-plasticity dilemma: the higher the proportion of retained old samples, the better the protection of old tasks but the lower the learning efficiency for new tasks, and storage budgets accumulate continuously over long task sequences.77% similarUnverifiedUnder multiple evaluation settings, filtering out historical events related to stable memories can maintain similar training loss trajectories.76% similarUnverifiedFine-tuning and persistent memory solve different problems: fine-tuning changes model behavior through training samples, while persistent memory lets the application store information externally and retrieve it when needed.75% similarUnverifiedDuplicate content in training data can cause model memorization and performance degradation.75% similarUnverifiedTo mitigate catastrophic forgetting, practices often use methods such as replay buffers to retain historical samples, elastic weight consolidation to constrain the magnitude of updates to important parameters, or parameter-efficient fine-tuning (such as LoRA).74% similar
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