Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning
Post-training expert pruning is a promising deployment technique for Mixture-of-Experts (MoE) models. The calibration set is an integral part of the pruning process, however, its correlation with evaluation performance is largely misunderstood and unintuitive. In this work, we present an empirical study of the robustness of calibration perturbations, specifically in MoE models. Showing how calibration size, language, and data distribution affect layer-wise expert pruning for gpt-oss-20b. Our findings reveal that (1) domain-general pruning quality receives minimal benefit from scaling calibration data beyond small sample sizes, (2) cross-lingual calibration transfers effectively, with Engl...
| Authors | Luis Frentzen Salim, Chia-Chun Wu, Tran Van Nhiem et al. |
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| Primary category | cs.CL |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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