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arXiv Machine Learning By Nicol\'as Mauricio Cuadrado \'Avila, Ivo Gollini Navarrete, Martin Tak\'a\v{c}, Samuel Horv\'ath

Building a Nested LADDER for LLM Unstructured Post-Training Pruning

Read the original on arXiv Machine Learning →

The paper introduces LADDER, a method for post‑training pruning of large language models that allocates sparsity incrementally across layers to minimize end‑to‑end degradation per removed parameter. LADDER produces non‑uniform sparsity profiles and nested masks for multiple budgets, and is evaluated on five OPT, Llama, and Qwen models at 10–90% sparsity using SparseGPT. Results show that while LADDER is comparable to uniform and adaptive baselines at low sparsity, it becomes substantially more robust from 70% sparsity onward, a benefit that largely persists after posterior distillation and improves zero‑shot accuracy retention.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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