PlexRL: Cluster-Level Orchestration of Serviceized LLM Execution for RLVR
Reinforcement learning with verifiable rewards (RLVR) has recently unlocked strong reasoning capabilities in large language models (LLMs), triggering rapid exploration of new algorithms and data. However, RLVR training is notoriously inefficient: long-tailed rollouts, tool-induced stalls, and asymmetric resource requirements between rollout and training introduce substantial idle time that cannot be eliminated by job-local optimizations such as synchronous pipelining, asynchronous rollout, or colocated execution. We argue that this inefficiency is structural. While idle gaps are unavoidable within individual RLVR jobs, they are largely anti-correlated across jobs and therefore exploitable...
| Authors | Yiqi Zhang, Fangzheng Jiao, Tian Tang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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