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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2606.00395 |
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| _version_ | 1866910273615429632 |
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| author | Dong, Daize Chen, Junlin Jia, Haolong Wu, Jiawei Di, Huanwei Liu, Jiang Wu, Jialian Liu, Zhengzhong Liu, Zicheng Barsoum, Emad Metaxas, Dimitris N. Wang, Hongyi |
| author_facet | Dong, Daize Chen, Junlin Jia, Haolong Wu, Jiawei Di, Huanwei Liu, Jiang Wu, Jialian Liu, Zhengzhong Liu, Zicheng Barsoum, Emad Metaxas, Dimitris N. Wang, Hongyi |
| contents | Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale. However, reinforcement learning (RL) on MoE-based LLMs often suffers from training instability. A root cause is router drift, i.e., expert activations can change drastically across model updates and differ between disaggregated rollout and training phases, causing large rollout--training mismatch and unstable importance sampling weights in PPO-style RL algorithms. Routing replay mitigates this issue by freezing the replay route within each reasoning trajectory, but it ignores how the router evolves under off-policy updates and thus causes router staleness. To address this limitation, we propose Predictive Routing Replay (PR2), which augments each router with a lightweight evolution predictor that learns to anticipate short-horizon router evolution. During the rollout phase, we use the predictive routing distribution to apply top-$k$ routing, enabling gradients to reach experts that are likely to become active after updates. During the training phase, we replay the resulting predicted route to retain consistency for stable importance estimation. Theoretical analysis and experiments support that PR2 reduces routing-induced mismatch, improves RL stability, and yields stronger performance across various reasoning benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00395 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning Dong, Daize Chen, Junlin Jia, Haolong Wu, Jiawei Di, Huanwei Liu, Jiang Wu, Jialian Liu, Zhengzhong Liu, Zicheng Barsoum, Emad Metaxas, Dimitris N. Wang, Hongyi Machine Learning Artificial Intelligence Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale. However, reinforcement learning (RL) on MoE-based LLMs often suffers from training instability. A root cause is router drift, i.e., expert activations can change drastically across model updates and differ between disaggregated rollout and training phases, causing large rollout--training mismatch and unstable importance sampling weights in PPO-style RL algorithms. Routing replay mitigates this issue by freezing the replay route within each reasoning trajectory, but it ignores how the router evolves under off-policy updates and thus causes router staleness. To address this limitation, we propose Predictive Routing Replay (PR2), which augments each router with a lightweight evolution predictor that learns to anticipate short-horizon router evolution. During the rollout phase, we use the predictive routing distribution to apply top-$k$ routing, enabling gradients to reach experts that are likely to become active after updates. During the training phase, we replay the resulting predicted route to retain consistency for stable importance estimation. Theoretical analysis and experiments support that PR2 reduces routing-induced mismatch, improves RL stability, and yields stronger performance across various reasoning benchmarks. |
| title | PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2606.00395 |