When running multiple RL training pipelines on shared inference hardware, intelligent scheduling of which requests to accept and how to allocate GPU memory between them can dramatically improve efficiency without compromising training quality.
This paper addresses a scheduling problem in LLM inference services that run multiple types of reinforcement learning workloads simultaneously. Different RL approaches (RLVR, RLHF, agentic) have different memory and timing needs, causing them to compete inefficiently for GPU cache.