RippleKV allocates KV cache budgets across transformer layers by measuring how much perturbing each layer's value cache shifts the model's output distribution, and it reports the best average LongBench score among the compared compression methods at matched budgets.
InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 18724–18741
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RippleKV: Cross-Layer KV Cache Allocation via Perturbation Propagation
RippleKV allocates KV cache budgets across transformer layers by measuring how much perturbing each layer's value cache shifts the model's output distribution, and it reports the best average LongBench score among the compared compression methods at matched budgets.