Ben Recht critiques the traditional view of rewards in reinforcement learning, arguing that rewards should be seen as internal to the agent rather than external signals from the environment. He believes this shift in perspective allows for more flexibility in how agents interpret their actions and adapt their goals. The change can enhance understanding and implementation in RL systems.