PEARL: Adaptive Prefill-Decode Execution with Elasticity for Agentic Reinforcement Learning
Multi-turn rollout dominates the cost of agentic reinforcement learning (RL). Asynchronous execution and elastic GPU resources can accelerate this stage, but adding rollout replicas yields diminishing returns while training GPUs remain idle between updates. We observe that effective resource use also depends on the prefill--decode (PD) configuration. Both the choice between colocation and disaggregation and the optimal PD ratio vary with the workload, making resource scaling and PD configuration interdependent. Exploiting this opportunity requires selecting effective configurations and realizing their benefits within transient resource-availability windows despite reconfiguration costs. W...
| Authors | Jiaan Zhu, Wei Gao, Youhui Bai et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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