PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning

📰 ArXiv cs.AI

Learn how PR2 improves MoE-based LLM reinforcement learning by reducing router drift and training instability

advanced Published 2 Jun 2026

Action Steps

Implement PR2 to reduce router drift in MoE-based LLMs
Use predictive routing replay to improve training stability
Apply PR2 to PPO-style RL algorithms to reduce rollout-training mismatch
Configure importance sampling weights to mitigate instability
Test PR2 on various MoE-based LLM architectures to evaluate its effectiveness

Who Needs to Know This

Researchers and engineers working on large language models and reinforcement learning can benefit from this technique to improve training stability and performance

Key Insight

💡 PR2 reduces router drift and training instability in MoE-based LLMs, leading to improved performance and stability

Key Takeaways

Learn how PR2 improves MoE-based LLM reinforcement learning by reducing router drift and training instability

Full Article

Title: PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning

Abstract:
arXiv:2606.00395v1 Announce Type: cross Abstract: 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 algo

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