arXiv AI August 28, 2026 By Francesco Pio Monaco, Elia Cunegatti, Flavio Vella, Giovanni Iacca

Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

The paper introduces ZipCal, a model‑agnostic data curation method that selects calibration data for post‑training compression of large language models by maximizing lexical diversity using Zipfian power laws. ZipCal outperforms uniform random sampling on pruning benchmarks and matches a state‑of‑the‑art perplexity‑based approach while being roughly 240× faster due to its linear complexity. The authors provide code and experiments at their GitHub repository.

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