arXiv:2606.00395v1 Announce Type: new 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 algorithms. Routing replay mitigates this issue by freezing the replay route within each reasoning traject
The rapid advancement and scaling of Large Language Models (LLMs) have brought Mixture of Experts (MoE) architectures to the forefront, making their training inefficiencies and instabilities a critical bottleneck for further progress.
Improving the stability and efficiency of training MoE-based LLMs through methods like Predictive Routing Replay (PR2) directly impacts the capabilities and accessibility of advanced AI, accelerating the development of more complex AI systems and agents.
This research outlines a method to mitigate critical training instabilities in MoE LLMs, potentially leading to more robust and powerful models with reduced computational overhead for development.
- · AI researchers
- · LLM developers
- · Cloud providers
- · Companies deploying AI agents
- · Less efficient LLM architectures
- · Organizations without access to advanced training techniques
More stable and efficient training of MoE LLMs will unlock new performance benchmarks and reduce compute costs, fostering wider adoption.
Improved LLM capabilities will accelerate the development and deployment of sophisticated AI agents across various sectors, automating complex workflows.
The proliferation of advanced AI agents could amplify geopolitical competition around AI supremacy, potentially leading to 'sovereign AI' initiatives being further emphasized.
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Read at arXiv cs.LG