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Gradual transitions in training objectives can significantly enhance model performance during adaptation, preserving valuable learned features.
Forget catastrophic forgetting: modular memory, blending in-context and in-weight learning, offers a practical path to truly continual learning agents.
Post-training LLMs is more about finding the right "key" (a few kilobytes of parameters) to unlock pre-existing knowledge than learning new information.
