AI RESEARCH
AIMER: Calibration-Free Task-Agnostic MoE Pruning
arXiv CS.LG
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ArXi:2603.18492v1 Announce Type: new Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token compute, but the deployment still requires storing all experts, making expert pruning important for reducing memory and serving overhead. Existing task-agnostic expert pruning methods are typically calibration-dependent: they estimate expert importance from routing or activation statistics on a calibration set, which makes pruning outcomes sensitive to the choice of calibration set and adds substantial preprocessing cost. We.