What happens when the experience buffer becomes the bottleneck in distributed reinforcement learning?

Distributed reinforcement learning grows by running many actors and many learners in parallel. They meet at an experience buffer that stores every transition until a learner can use it.

As the job gets bigger the buffer stops being a simple queue. It turns into the storage layer of a large-capacity, latency-critical path that every training iteration must cross to move, transform, sample, and batch experiences before the learner update begins. This path is now the system's hot spot.

Current RL systems handle this path in two ways. Some bake the whole experience flow into the framework control flow. Others expose a request-driven buffer service. Both fix experience placement and make it hard to schedule experience-path work as an independent runtime optimization target.

Systems thinking reframes this. When placement is fixed, data sits where the framework first put it — often on the wrong memory tier or the wrong node. When scheduling is implicit, experience handling runs whenever the framework decides, adding latency the learner must wait through.

The runtime layer that takes over data decisions

The Experience Data Plane (EDP) is a runtime abstraction that separates RL experience-handling semantics from framework-specific execution logic. EDP exposes three explicit control points: experience ingestion, experience placement, and experience delivery.

Ingestion accepts transitions from actors without dictating where they land. Placement decides which CPU or GPU memory tier on which node holds each piece of experience state. Delivery serves sampled batches to learners when they are ready. The framework no longer controls these steps; the runtime does. EDP is framework-agnostic. The paper demonstrates integration with RLlib without changing RLlib's framework execution logic. The framework still defines the algorithm; Conduit decides where and when the data lives and moves.

Where experience lives across the cluster

Capacity-constrained, bandwidth-aware placement distributes experience state across CPU and GPU memory tiers and across nodes. It respects heterogeneous interconnect speeds and device-memory limits. If a GPU has limited memory but high bandwidth to a CPU with large memory, the runtime keeps hot batches on the GPU and spills the rest to the CPU.

When the data path runs

Latency-aware scheduling controls when experience-path handling runs. It overlaps data movement, transformation, sampling, and batching with other work so the exposed experience-path latency — the