Router Drift Control: Mitigating Off-Policy Degradation in Mixture-of-Experts Reinforcement Learning

Reinforcement learning (RL) can become off-policy due to training-inference mismatch, mini-batch updates, and asynchronous training.The resulting mismatch is especially harmful in Mixture-of-Experts (MoE) models, where rollout--training discrepancies can change routing decisions and lead to router d