ResearchResearch paperLarge Language Models1 source · Oct 20, 2026

Building a Nested LADDER for LLM Unstructured Post-Training Pruning

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 profiles and exactly nested masks across multiple budgets.
We evaluate LADDER on five OPT, Llama, and Qwen models across 10-90% sparsity, using SparseGPT as its candidate-generation backend and comparing against uniform and adaptive allocation baselines.

[1]Building a Nested LADDER for LLM Unstructured Post-Training Pruning
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 06:10 PM
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.

Extractive summary: sentences quoted from the sources.

Before this