SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication

TL;DR

SparseRL-Sync introduces a lossless, sparse weight synchronization method for large-scale reinforcement learning, achieving approximately 100x reduction in communication volume by exploiting high sparsity in parameter updates.

Contribution

The paper presents SparseRL-Sync, a novel method that replaces full-weight transfers with lossless sparse updates, significantly reducing communication overhead in bandwidth-constrained RL systems.

Findings

Achieves about 100x reduction in data transmission with 99% sparsity.

Maintains 100% fidelity of weight updates through lossless sparse encoding.

Improves scalability and efficiency in bandwidth-limited RL deployments.

Abstract

In large-scale reinforcement learning (RL) systems with decoupled Trainer-Rollout execution, the Trainer must regularly synchronize policy weights to the Rollout side to limit policy staleness. When inter-node bandwidth is abundant, such synchronization is usually only a small fraction of end-to-end cost. As model size grows, however, the communication demand rises rapidly. In bandwidth-constrained or network-variable deployments -- for example, cross-datacenter or cross-cluster settings, heterogeneous resource pools, and online RL -- weight synchronization can become a dominant bottleneck for throughput and tail latency. We observe that, in mainstream large-model RL training, the locations where parameters actually change are highly sparse at the element level (often 99%+ sparsity). Building on this observation, we propose and implement SparseRL-Sync, which replaces full-weight transfers with a lossless sparse update payload (indices and values) that can be exactly reconstructed on the inference side, thereby preserving 100% fidelity. Under a simplified cost model, sparse synchronization reduces the per-update communication volume from S to approximately S/X; with 99% sparsity (X ~ 100), this yields about a 100x reduction in transmitted data. Combined with appropriate bucketing, SparseRL-Sync also reduces launch and control-plane overhead, significantly improving scalability and end-to-end efficiency in bandwidth-limited and highly asynchronous RL settings.