Case context: A team has a deep learning model using dropout, with the dropout rate tuned to perform best on the dev set. They want lower bias and are considering a much larger network, but are hesitant because in the past, without regularization, bigger models led to overfitting. Compute resources are limited but available.

Question: Based on the source's reasoning, should the team's main hesitation be overfitting risk or something else? What should they weigh instead when deciding whether to increase model size?

Sample answer: Since the team already has a well-tuned regularization method (dropout tuned on the dev set), the source's reasoning suggests overfitting is not the primary risk here; a well-designed regularization approach usually lets model size increase without increasing overfitting, and performance usually stays the same or improves. Their real consideration should be computational cost, since that is the main reason the source gives for avoiding a bigger model once regularization is properly in place. They should weigh whether their available compute resources can support the larger model's training and inference costs, rather than fearing overfitting as the primary blocker.

Key points:

• Team already uses well-tuned regularization (dropout tuned on dev set)
• Overfitting risk from bigger models is reduced when regularization is well-designed
• Performance is expected to stay the same or improve with a larger model
• Computational cost, not overfitting, becomes the main deciding factor

Rubric: Full credit for identifying that regularization reduces overfitting concern and that computational cost is the main remaining consideration. Partial credit for only addressing one aspect.