A Survey of 16 Open-Source RL Libraries

As of 2026, among the 16 surveyed open-source RL libraries, the common points are that: (1) inference and training are disaggregated; (2) they are connected by a rollout buffer to temporarily store model outputs; (3) weights are transferred asynchronously. Other findings include: (1) Ray dominates orchestration; (2) NCCL broadcast is the default method for transferring model weights; (3) staleness management, referring to how to handle outdated data samples, ranges from least-recent method to advanced importance-sampling correction; (4) LoRA training is sparsely supported; (5) distributed MoE support is becoming trend. See more at Huggingface Blog