Due to the strong complementarity between visible light and infrared images, more attention has been focused on tracking through the joint information of these two modalities. However, in existing tracking algorithms, hthe inability to effectively learn the complementary information of both and mine modality-specific features limits the performance of the tracker. In responseto this issue, a reversible multibranch bimodal adaptive fusion network for tracking is proposed. Firstly, a tri-branch structured network is designed for separate learning of thermal infrared, visible light, and their shared characteristics. This design not only maximizes the utilization of shared modal information, but also preserves the differential characteristics between infrared and visible data as well as the rich detail information. Furthermore, an adaptive module for modal feature interaction is introduced to efficiently mine complementary modal information and filter out redundant data. Extensive experiments conducted on multiple public datasets proves the effectiveness of this tracker, particularly showcasing remarkable anti-interference capabilities in scenarios involving scale changes, camera shakes, and occlusion.