Wikipedia has an unfortunate and incorrect-in-generality description of reinforcement learning (emphasis added)

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

Later in the article, talking about basic optimal-control inspired approaches:

The purpose of reinforcement learning is for the agent to learn an optimal, or nearly-optimal, policy that maximizes the "reward function" or other user-provided reinforcement signal that accumulates from the immediate rewards. This is similar to processes that appear to occur in animal psychology. For example, biological brains are hardwired to interpret signals such as pain and hunger as negative reinforcements, and interpret pleasure and food intake as positive reinforcements. In some circumstances, animals can learn to engage in behaviors that optimize these rewards.

Reward is not the optimization target. 

It's not really a surprise that (IMO) the alignment field has anchored on "reward is target" intuitions, given that the broader field of RL has as well. Given bad initialization, conscious effort and linguistic discipline is required in order to correct the initialization.