Optimizing Long-Tail and MoE Challenges in Reinforcement Learning with SGLang

Optimizing Long-Tail and MoE Challenges in Reinforcement Learning with SGLang - Chenyang Zhao, UCLA The increasing complexity of multi-turn interactions and the adoption of advanced architectures like Mixture-of-Experts (MoE) present significant performance and optimization challenges in Reinforcement Learning. This talk will introduce the SGLang RL group’s recent breakthrough. I will present our novel methods for mitigating long-tail rollout problems inherent in complex, multi-turn RL tasks, ensuring more robust and reliable model performance. Furthermore, we will detail our specialized acceleration techniques for the resharding process in MoE models, which dramatically reduce latency. Finally, we will provide an overview of the growing SGLang RL ecosystem and highlight our key partnerships, demonstrating the framework’s real-world impact and production-readiness for training and serving the next generation of models.