This paper presents a study on improving deepfake speech detection via post-training strategies. The authors explore adaptation techniques that fine-tune detection models after initial training, focusing on robustness against unseen spoofing attacks and cross-domain generalization. The proposed methods demonstrate strong performance and were accepted at ASRU 2025.