Comparative Analysis of 16 Open-Source RL Libraries

In Synchronous RL, the data generation (Inference) phase takes much longer than the training phase, causing a bottleneck where the GPU remains idle. To address this, the Asynchronous architecture has become standard, separating inference and training to operate distinct GPU pools, passing data through a Rollout Buffer, and transferring weights asynchronously.

HuggingFace conducted a comparative analysis of 16 open-source libraries based on 7 axes: orchestration, buffer design, weight synchronization protocol, Staleness management, LoRA support, and distributed training backend.

Key findings are as follows:

• Ray is used in half (8/16) of the surveyed libraries, showing overwhelming dominance as an orchestration tool.
• NCCL broadcast is used as the default method for weight transfer.
• Staleness management (data freshness management) is implemented in various ways, ranging from simply discarding stale samples to using Importance-sampling correction.
• LoRA support is still rare, and distributed MoE (Mixture of Experts) support is emerging as a key differentiator for next-generation libraries.