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Summary tokens are not just compressed context, but also dynamic KV cache indices, unlocking flexible memory compression and reduced compute in hybrid attention.
A smaller batch size and larger learning rate can lead to flatter minima in SAM, revealing a critical trade-off in hyperparameter tuning that impacts generalization.
Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.
