Rethinking Quality Tuning for Rectified Flow Models from a Rotational Perspective

Quality tuning (QT) is critical for diffusion and rectified-flow models. It adapts pretrained generators to curated high-quality data and supports later post-training. However, the pretraining-QT data gap may require more updates, while over-tuning on limited data can cause overfitting and lower generation quality. Standard QT also uses a fixed noise shift, so noise exposure remains unchanged during training. We revisit QT from a rotational perspective, motivated by two observations. First, earlier direction correction improves reconstruction, even though it affects more steps. Second, at similar noise levels, angular errors vary across examples. Based on these observations, we introduce Rotational Quality Tuning (RoQT). At the macro level, a noise curriculum anneals a strong high-noise shift through an intermediate shift toward the reference shift. At the micro level, angular weighting emphasizes difficult examples within each noise region. We further decompose the flow-matching mean-squared error into magnitude and rotational components, and leverage the rotational component to derive an explicit, scale-calibrated direction correction. RoQT improves convergence efficiency and generation quality while mitigating the performance degradation caused by over-tuning on limited data. Experiments on SD3.5-Medium and Z-Image demonstrate that RoQT consistently outperforms standard QT on GenEval and Qwen-Image-Bench under the same training data and matched inference settings. We will release the code and data on GitHub.