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Multi-byte prediction accelerates byte-level language model inference without sacrificing performance, achieving a breakthrough in generative task efficiency.
Naive fine-tuning leads to catastrophic forgetting, but combining replay-based and parameter isolation strategies can actually *improve* performance over joint training in continual learning for intent classification.
