What do you mean? Is post-training/RL not training? It seems to me that inference is quite stable there and only change being more time on RL which I would still categorize as training.
You got it backwards. RL is long running inference plus training at the end. They let the Agent spin for quite a while, then they calculate the reward afterwards and modify the parameters accordingly.
... so training? Even "regular" pretraining is "inference plus training at the end" if you want to frame it that way
Within the frame of that discussion, the important distinction is whether the calculation is done for a paying customer, or for model improvement. You can choose to make separate buckets for RL and training (that's interesting to understand the evolution of model creation techniques), but it won't change the end user.