Reinforcement learning works by gradient ascent, we take the gradient of the reward function with respect to the model's parameters - that is, we find the direction in parameter space by which the reward would be most quickly increased at that point, and take a small step in that direction. We do not actually "reward" or "punish" the model. "Reward" is a technical term which does not refer to the same thing as in colloquial English.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence