Async RL training with disaggregated inference in TRL using vLLM, Ray, and NCCL

AI Impact Summary

Open-source RL pipelines suffer from GPU idle during long inference rollouts in synchronous training. The recommended pattern is to disaggregate inference and training onto separate GPU pools, use a rollout buffer for data exchange, and perform weight synchronization asynchronously to overlap work. The article benchmarks 16 libraries and highlights Ray for orchestration and NCCL for weight transfers, with staleness handling and LoRA support as critical tradeoffs. For engineering teams, adopting this pattern implies rearchitecting training pipelines (e.g., TRL/GRPOTrainer, vLLM/SGLang) and provisioning additional GPUs and networking to realize the throughput gains.