Tracking-by-propagation has emerged as a promising paradigm in Multi-Object Tracking (MOT), leveraging feature propagation across frames to maintain object identities within an end-to-end architecture. Existing approaches, such as SambMOTR, have demonstrated impressive performance, often rivaling or surpassing tracking-by-detection methods, all while utilizing a single fully trainable architecture based on DETR-like detectors. However, their effectiveness is limited by several important challenges. Jointly training a strong pre-trained detector and a propagation module from scratch often disrupts the detector's initialization, resulting in performance degradation. Additionally, tracking-by-propagation struggles to maintain tracklet consistency during long occlusions, leading to identity switches and deteriorating tracking performance. To address these limitations, we propose SambaMOTRv2, a novel framework that enhances tracking-by-propagation performance by integrating within SambaMOTR a memory- and time-efficient pre-training strategy for the propagating parts of the algorithm and a Samba-based re-identification system.