Recent reinforcement learning advances for Mixture-of-Experts (MoE) large language models have focused on improving optimization stability and training efficiency while treating expert selection as a fixed component, according to a new paper on Expert-Space Exploration in MoE Reinforcement Learning. The work targets the routing mechanism that determines which experts process each token, an aspect prior MoE RL research left unaddressed.
Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models have primarily focused on improving optimization stability and training efficiency, while treating the expert selection as a fixed component. Since rout
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