A machine learning engineer is training a reward model where the goal is to align the model's predicted scores, $r(\mathbf{x}, \mathbf{y})$, with human-provided scores, $\varphi(\mathbf{x}, \mathbf{y})$. The standard approach is to maximize the objective function $\mathcal{L} = -\mathbb{E}[\varphi(\mathbf{x}, \mathbf{y}) - r(\mathbf{x}, \mathbf{y})]^2$. Suppose the engineer makes a mistake and instead configures the training process to maximize the standard mean squared error, effectively removing the negative sign from the objective: $\mathcal{L}_{mistake} = \mathbb{E}[\varphi(\mathbf{x}, \mathbf{y}) - r(\mathbf{x}, \mathbf{y})]^2$. What would be the most likely effect on the model's behavior during training?

Reward Model Objective Calculation

Pointwise Rating Loss (L_rating) Formula

In the context of training a model to predict scores for a given input-output pair, consider the following objective function: $\mathcal{L} = -\mathbb{E}[\varphi(\mathbf{x}, \mathbf{y}) - r(\mathbf{x}, \mathbf{y})]^2$ Match each component of the formula to its correct description.