RAZOR: Pruning Replaceable Experts in LLMs

תקציר מקורי באנגליתarXiv:2609.30465v2 Announce Type: new Abstract: Mixture-of-experts (MoE) models activate only a few experts per token yet store the entire expert pool. Whole-expert pruning shrinks that pool, but for reasoning models it must remove experts without eroding reasoning ability. Common scores rank experts by routing frequency or output magnitude, which measures isolated contribution rather than deletion damage. What decides the damage is functional replaceability, whether the surviving computation can reproduce what is removed. A large contribution may be replaceable by the remaining mixture, whereas a small one may carry a direction the survivors cannot recover. We introduce RAZOR, a training-free method that scores replaceability from consensus residuals, the deviations of individual expert o