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Self-distillation in CTR prediction simplifies to a logit-averaged loss plus a disagreement penalty, enabling sample-free training and boosting single-tower accuracy.
Summary tokens are not just compressed context, but also dynamic KV cache indices, unlocking flexible memory compression and reduced compute in hybrid attention.