A reward model is being trained on a dataset of human preferences. For one specific data point in the dataset, the model is given a prompt ($x$), a human-preferred response ($y_a$), and a human-dispreferred response ($y_b$). The model assigns the following scalar scores:

• Score for preferred response, $r(x, y_a) = 2.0$
• Score for dispreferred response, $r(x, y_b) = 2.0$

The loss for this single data point is calculated using the formula: $\mathcal{L} = - \log \sigma(r(x,y_a) - r(x,y_b))$

Where $\sigma$ is the sigmoid function, $\sigma(z) = 1 / (1 + e^{-z})$, and $\log$ is the natural logarithm.

Calculate the loss value $\mathcal{L}$ for this specific data point. Explain the significance of the resulting loss value in the context of training this model. (You may use the approximation $\ln(0.5) \approx -0.693$).