Stabilizing MoE Reinforcement Learning by Aligning Training and
  Inference Routers
Authors: name: Wenhan Ma, name: Hailin Zhang, name: Liang Zhao, name: Yifan Song, name: Yudong Wang, name: Zhifang Sui, name: Fuli Luo
Organization: Xiaomi MiMo

Reinforcement learning (RL) has emerged as a crucial approach for enhancing
the capabilities of large language models. However, in Mixture-of-Experts (MoE)
models, the routing mechanism often introduces instability, even leading to
catastrophic RL training collapse. We analyze the training-inference
consistency of MoE models and identify a notable discrepancy in routing
behaviors between the two phases. Moreover, even under identical conditions,
the routing framework can yield divergent expert selections across repeated
forward passes. To address this foundational inconsistency, we propose Rollout
Routing Replay (R3), a method that records routing distributions from the
inference engine and replays them during training. R3 significantly reduces
training-inference policy KL divergence and mitigates extreme discrepancies
without compromising training speed. Extensive experiments on various settings
confirm that R3 succeeds in stabilizing RL training, preventing collapse and
outperforming methods such as GSPO and TIS. We believe this work can offer a
new solution for stabilizing RL in MoE models.