Building a Nested LADDER for LLM Unstructured Post-Training Pruning
arXiv:2610.28773v1 Announce Type: new Abstract: Non-uniform allocation can improve post-training pruning of large language models, yet quality can still degrade sharply at high sparsity. To control degradation as sparsity increases, we introduce LADDER (Layer-Wise Allocation via Damage-Driven Evaluation and Refinement), which allocates sparsity incrementally. At each step, it evaluates pruning an additional fraction of weights from each eligible layer and commits the layer update that causes the least end-to-end degradation per removed parameter. This process produces non-uniform sparsity prof
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