Faster Synchronous On-Policy RL via Straggler-Aware Group Sizing

arXiv:2606.02218v1 Announce Type: new Abstract: Synchronous reinforcement learning methods such as Group Relative Policy Optimization (GRPO) provide stable and reproducible on-policy training, but they are highly vulnerable to stragglers, a single unusually long rollout can delay reward computation and parameter updates for the entire group. This problem becomes more severe as group size increases, creating a tension between the benefits of larger groups and the wall-clock cost of synchronization stalls. We propose Straggler-Aware Group Control (SAGC), a dynamic group-size controller that adap

Source: arXiv cs.LG — read the full report at the original publisher.

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