Expert-Space Exploration in MoE Reinforcement Learning
ESRL is introduced, an architecture-aware framework that explicitly explores the expert-routing space of MoE models, and preserves high-confidence experts as anchors, and restricts stochastic routing to a plausible candidate pool, thereby retaining reliable computation paths.
Hong-Yi He, Zheng-Wen Lin, Xiao Liu et al.
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