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Retaining past knowledge can actually impede real-time adaptation in dynamic environments, leading to a new framework for optimizing continual learning.
Gradual transitions in training objectives can significantly enhance model performance during adaptation, preserving valuable learned features.
Low-dimensional gradient dynamics in Feedback Alignment limit deep learning performance, but orthogonalization techniques can boost accuracy by up to 9 percentage points.
LLMs can fake stochasticity with random seeds, but their direct sampling from distributions is fundamentally broken.
