Optimizing Reinforcement Learning at Trillion-Parameter Scale - Songlin Jiang

Optimizing Reinforcement Learning at Trillion-Parameter Scale - Songlin Jiang, Aalto University & Mind Lab This talk will dive into how we implemented and optimized reinforcement learning on trillion-parameter Mixture-of-Experts reasoning models using veRL, Megatron-Bridge and vLLM. The session is useful to anyone building large-scale RL training systems. For the first part, I will walk through the system design required to make RL work at this scale using LoRA: how LoRA adapters are implemented for expert layers, how adapters are sharded and fused under tensor/pipeline/expert parallelism, and most importantly, how refit (parameter sync) is implemented for LoRA between training backend (Megatron) and rollout engine (vLLM). The second part of the talk focuses on training–inference mismatch in MoE RL. I will explain why common mitigations such as clipping and importance sampling can fail, and how we implement fixed Router Replay R3 across vLLM, veRL, and Megatron to align routing decisions between rollout and training. These works are done together with Mind Lab and some of the related blog posts are at: - https://macaron.im/mindlab/research/building-trillion-parameter-reasoning-rl-with-10-gpus - https://macaron.im/mindlab/research/router-replay-r3-why-it-failed-and-how-we-fixed-it