[
    {
        "id": "osp-22219",
        "type": "article-journal",
        "title": "Tilted Schrödinger Bridge Matching",
        "author": [
            {
                "family": "Kholkin",
                "given": "Sergei"
            },
            {
                "family": "Burnaev",
                "given": "Evgeny"
            },
            {
                "family": "Korotin",
                "given": "Alexander"
            }
        ],
        "URL": "https://omanscience.com/en/articles/tilted-schr-dinger-bridge-matching",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Schrödinger bridges provide an entropy-regularized framework and a principled solution for unpaired domain translation. In practice, a pretrained bridge may need to be adapted to human preferences or physical constraints through a reward a problem closely related to reward tilting in diffusion models but underexplored for Schrödinger bridges. We introduce Tilted Schrödinger Bridge Matching (TSBM), a post-training method for fine-tuning a learned bridge $P$ between source $p_0$ and target $p_1$ toward a reward-tilted target $p_1^r\\propto p_1e^r$, while preserving source $p_0$. We formulate this adaptation as alternating optimization initialized from $P$, provide theoretical justification, and derive a practical algorithm based on Adjoint Matching. We evaluate TSBM on unpaired image-to-image translation targeting digit properties in MNIST and facial attributes in CelebA."
    }
]