Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained compelling results. These methods use a learned (flow) model to transport an initial standard Gaussian noise that ignores the condition to the conditional data distribution. The model is hence required to learn both mass transport and conditional injection. To ease the demand on the model, researchers propose Condition-Aware Reparameterization for Flow Matching (CAR-Flow) – a lightweight, learned shift that conditions the model to align the source and target for better flow matching. This approach helps alleviate the burden on the model by simplifying the learning process and improving flow matching performance in conditional generative modeling tasks.