Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries

Synchronous reinforcement learning (RL) training suffers from a major bottleneck where data generation via inference leaves training GPUs idle for long periods. The widely adopted solution is to disaggregate inference and training onto separate GPU pools, using a rollout buffer to allow both processes to run asynchronously and maximize hardware utilization. A survey of 16 open-source libraries implementing this pattern was conducted to compare their architectural choices across seven axes. Key findings show a dominance of the Ray framework for orchestration and NCCL for weight synchronization, while highlighting sparse support for LoRA and the emergence of distributed MoE as a key differentiator.