An engineer is building a text classifier for a specific task, such as identifying spam emails. The model architecture consists of a large, pre-trained language model followed by a new classification layer. During training on a labeled dataset of emails, the parameters of both the pre-trained model and the new classification layer are adjusted simultaneously to maximize the probability of predicting the correct labels ('spam' or 'not spam'). Which of the following statements best analyzes the primary purpose of adjusting the pre-trained model's parameters in this setup?

You are training a text classification model that uses a large, pre-trained language model as its base, combined with a new prediction network on top. Arrange the following steps of a single end-to-end training iteration in the correct chronological order.

Optimizing a Sentiment Analysis Model