Training data ambiguity, such as the presence of out-of-frame moving objects, introduces significant challenges in deep learning-based optical flow models by causing large loss spikes and training instability. Most models overlook this ambiguity, treating it as a limitation of existing datasets. SEA-RAFT attempts to address these ambiguous areas with an additional network and a modified loss function, yet does so without explicitly verifying the specific drawbacks. In this paper, we investigate the influence of out-of-frame movements on model accuracy by generating the FlyingIcons dataset, which includes in-frame and out-of-frame masks for precise analysis. Using the latter masks, we introduce a weighted masked training scheme that selectively penalizes errors in out-of-frame areas, significantly increasing model accuracy over both standard GMA training and SEA-RAFT. Building on this concept, we propose a weighted partially masked training method, which uses partial out-of-frame masks generated through a simple process that adds the ground truth flow to pixel locations and checks if they fall outside the frame. While this method only yields improvements in error reduction on FlyingThings3D, our findings suggest that incorporating similar masks into other synthetic datasets could improve model stability and accuracy with minimal additional overhead. This highlights a promising direction for further research, particularly in developing more complex mask generation strategies and creating synthetic datasets with out-of-frame masks to enhance generalizability across datasets.