Unverified50% confidenceSolutionExact time
Technical measures for mitigating model memorization issues include data deduplication, differentially private training, and output filters.
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9/6/2026
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Unverified差分隐私通过在训练数据中注入校准噪声，确保模型无法记忆单个用户的代码片段76% similarUnverifiedMethods such as freezing LLM backbone parameters during the visual alignment training phase, staged training, data ratio replay, and LoRA can mitigate catastrophic forgetting in multimodal training.72% similarUnverifiedOff-policy algorithms (such as SAC and DQN) can be trained on data collected by any policy, are capable of maintaining a replay buffer of millions of experiences for repeated learning, and have higher sample efficiency than on-policy methods.70% similarUnverifiedUpdating parametric memory requires fine-tuning or continual pre-training, both of which require additional training processes and computational resources.69% similarUnverified缓解灾难性遗忘的策略包括弹性权重巩固（EWC）、数据混合回放、控制学习率与训练步数以及使用 LoRA 等参数高效方法69% similar
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