Moreover, all these reward approaches are fixed and do not change over training, and all the metrics may not be important over every stage of the training.
it might be useful to consider using a dynamic combination of metrics, which rewards to use early vs. later, or which rewards might be useful to come back later in training, and consider the context of the full history of rewards, as well as the models current state and the nature of the metric.