We support three training objectives: Ranking, Generation, and Combined. The Ranking objective uses a learning-to-rank algorithm to output the logits for the highest-ranked passage ID. The Generation objective follows the principles of Causal Language Modeling, focusing on permutation generation. The Combined objective, which we introduce in our paper, is a novel weighted approach that seamlessly integrates both ranking and generation principles, and is the setting applied to FIRST model.