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Replay mixing is a regularization strategy in continual learning: when fine-tuning for a new task or domain, a portion of old data is deliberately retained and given a higher sampling weight to prevent the model from forgetting old capabilities.
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10/17/2026
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UnverifiedA hybrid strategy—using short-term context for immediate conversation, vector retrieval to supplement background knowledge, and structured summaries to maintain long-term state—often outperforms a single memory solution.74% similarUnverifiedThis approach embodies a compute-shifting philosophy that moves the computational burden from repeated test attempts to a one-time learning investment.73% similarUnverifiedIn the reinforcement learning pipeline, the model continuously generates candidate outputs (rollouts), which are evaluated and scored before being fed back into the training process to update the policy.72% similarUnverifiedReplay data refers to general dialogue samples mixed in at a certain ratio during domain fine-tuning, the purpose of which is to prevent the model from overfitting on narrow-domain data and losing its general dialogue ability (catastrophic forgetting).72% similarUnverifiedThe hybrid strategy involves first using public datasets to complete prototypes and baseline models, then targeted self-collection of edge-case data for incremental training, and finally continuously collecting hard samples in the real deployment environment for iterative improvement.72% similar
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