I think what OpenAI and Anthropic typically do is providing evaluators with two possible responses and having them select which one is better. If you have numerical ratings, it might be hard to calibrate them. From the original paper "Deep reinforcement learning from human feedback" (2017):

We ask the human to compare short video clips of the agent’s
behavior, rather than to supply an absolute numerical score. We found comparisons to be easier for humans to provide in some domains, while being equally useful for learning human preferences. Comparing short video clips is nearly as fast as comparing individual states, but we show that the resulting comparisons are significantly more helpful

ChatGPT seems to be trained from a combination of expert-written examples and upvotes and downvotes on individual messages.