SparseRL-Sync: ~100x Less Communication for Weight Sync in RL
A new method called SparseRL-Sync reduces communication costs for weight synchronization in large-scale reinforcement learning systems by up to 100x. In decoupled Trainer-Rollout architectures, the Trainer must regularly sync policy weights to the Rollout side to prevent policy staleness. As model sizes grow, communication demand rises, becoming a bottleneck in bandwidth-constrained or network-variable deployments like cross-datacenter settings, heterogeneous resource pools, and online RL. The key observation is that parameter changes are highly sparse at the element level, often exceeding 99% sparsity. SparseRL-Sync replaces full-weight transfers with a lossless sparse update payload consisting of indices and values of changed parameters. The paper is available on arXiv under ID 2605.07330.
Key facts
- SparseRL-Sync reduces communication for weight synchronization in RL by ~100x.
- It targets decoupled Trainer-Rollout systems where the Trainer syncs weights to Rollout.
- Parameter changes are often >99% sparse at the element level.
- The method uses lossless sparse update payloads instead of full-weight transfers.
- It addresses bottlenecks in cross-datacenter, cross-cluster, and online RL deployments.
- The paper is published on arXiv with ID 2605.07330.
- The approach is lossless, meaning no accuracy is sacrificed.
- It is designed for bandwidth-constrained or network-variable environments.
Entities
Institutions
- arXiv