Eye-Tracking (ET) data provide valuable insights into human language processing and have recently been employed across a wide range of fields, from psycholinguistics to NLP. In this work, we introduce CERVINO, the largest Italian eye-tracking-while-reading dataset to date, comprising data from more than 200 participants reading legal texts, recorded at 120Hz. Our analyses show that the dataset captures established psycholinguistic effects: reading times are influenced by word length, lexical frequency, and surprisal. We further evaluate the usefulness of the dataset in downstream tasks by fine-tuning BERT on ET measures. Results show improved alignment between model attention and human reading behavior while preserving downstream task performance. Moreover, combining our data with higher-frequency ET resources yields the strongest alignment results, suggesting that lower-frequency ET data provide useful cognitive signals. Overall, these findings indicate that lower-frequency ET data retain sufficient cognitive information to provide useful supervision for attention alignment and complement existing high-frequency resources.