In a reinforcement learning process that uses human feedback, a 'reference model' with a fixed set of parameters, $\theta_{\text{ref}}$, is used as a baseline. For a specific input prompt, this model calculates that the probability of generating the word 'consequently' as the next word is 0.04. Given that the reference policy, $\pi_{\theta_{\text{ref}}}(\cdot)$, is formally defined as the probability distribution generated by this reference model, what is the value of $\pi_{\theta_{\text{ref}}}(\text{'consequently'})$?

True or False: In a Reinforcement Learning from Human Feedback (RLHF) system, the reference policy $\pi_{\theta_{\text{ref}}}(\cdot)$ is a function that is trained to approximate the probability distribution $\text{Pr}_{ heta_{\text{ref}}}(\cdot)$ generated by the reference model.

Reference Policy and Model Probability