Analyzing the Impact of Positional Bucket Size on Model Behavior

A machine learning engineer is training a T5-style model and observes that its performance on the training dataset is excellent, but its performance on a held-out validation dataset is poor. This suggests the model is overfitting. Based on the role of positional bias buckets as a regularization technique, which of the following actions would be the most appropriate first step to address this issue?

When training a model that groups various token-to-token offsets into a limited number of 'buckets' to learn relative positional information, continually increasing the number of buckets is a reliable strategy for improving the model's generalization performance on unseen data.