Conduit: An Experience Data Plane for Distributed Reinforcement Learning
Conduit turns the reinforcement-learning experience buffer into a runtime-managed data plane.
The paper says RL scaling is increasingly bottlenecked by the path that moves, places, samples, and batches experience before learner updates. Conduit exposes ingestion, placement, and delivery as explicit control points, separate from framework execution logic. Integrated with RLlib, it reports up to 97% lower exposed experience-path latency, up to 38% lower end-to-end iteration latency, scaling to 1,024 GPUs while preserving convergence. ArXiv · AI/CL/LG's note
The paper says RL scaling is increasingly bottlenecked by the path that moves, places, samples, and batches experience before learner updates. Conduit exposes ingestion, placement, and delivery as explicit control points, separate from framework execution logic. Integrated with RLlib, it reports up to 97% lower exposed experience-path latency, up to 38% lower end-to-end iteration latency, scaling to 1,024 GPUs while preserving convergence. ArXiv · AI/CL/LG's note
score 5