Resolving Endpoint Underfitting in Diffusion Bridges via Noise Alignment

ArXi:2605.28962v1 Announce Type: new Diffusion bridge models offer a powerful framework for connecting two data distributions, such as in image restoration and translation. Many existing methods learn this bridge by mimicking the score-matching formulation of standard diffusion models. In this work, we find that this way leads to an anomalous underfitting phenomenon near the target endpoint, as the process approaches the target distribution ($t \to 0