Reinforcement learning pipelines for large language models now often merge rollout workloads from multiple domains and feedback systems, increasing complexity. However, existing prefix-aware routing methods, which enhance inference efficiency through cache reuse and load balancing, fail to regulate competition for KV-cache capacity among diverse rollout sessions. Researchers have proposed scheduling mixed RL rollouts beyond prefix locality to address this issue1. This approach aims to optimize the allocation of KV-cache capacity, reducing conflicts between heterogeneous rollout sessions and improving overall efficiency. The development of more efficient RL pipelines has significant implications for the security of large language models, as it can impact their capability and risk surfaces. As reinforcement learning continues to shape the development of LLMs, understanding the security implications of these advancements is crucial for practitioners. The ability to efficiently manage rollout workloads will be essential in mitigating potential security risks associated with LLMs.