The opportunity with RL-enabled post-training is to build a much more integrated platform across training, inference, data, environments and agents.
The post-training loop now includes rollout workers, environments, reward models and verifiers, policy optimization, and continuous evaluation. It looks less like one distributed training job and more like a distributed application. That brings new needs around heterogeneous compute, weight synchronization, checkpointing, preemption and selective sampling.
This is why systems like Ray are becoming more relevant around the PyTorch/DeepSpeed/Megatron/JAX training stack, with Kubernetes primitives like Kueue and JobSet handling scheduling underneath. For agentic workloads, the same tools, sandboxes and environments used during training also need to work consistently with the broader agent platform.