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CaRE-KD is proposed, a confidence-gated distillation framework that replaces static objectives with uncertainty-adaptive optimization and provides a gradient-level analysis showing how this dual-granularity design induces a conditional calibration mechanism that prior static divergences cannot reproduce.
SNIPER is presented, a two-stage structured pruning framework that solves a knapsack optimization over coarse-granularity components to yield conditionally optimal parameter allocations with respect to fixed importance estimates, followed by a fine-grained pruning stage to meet strict budget constraints.
This work introduces pruning laws, simple and interpretable scaling relations that connect a pruned LLM's post-pruning performance to its unpruned performance and pruning ratio, and demonstrates that the functional form transfers across dense and mixture-of-experts architectures, pruning methods, and unseen models in z...
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