cs.LGOct 13, 2026

BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling

Authors: Mengyu Li, Qianqian Qu, Jun S. Liu

Organizations: Department of Statistics and Data Science, Tsinghua University, Beijing, China

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Abstract

Generative models are often required to serve many downstream conditional tasks. In the training-free setting, a pretrained unconditional model is reused as a prior and combined with a task-specific likelihood to sample from the corresponding posterior. While such training-free samplers have been developed for diffusion and flow models, their counterpart for Schrödinger bridge (SB) models remains unexplored, despite the growing appeal of SBs as flexible stochastic transport models between general source and target distributions. We propose BridgeTwist, a training-free conditional sampler for pretrained SB generative models, formulated as a tempered twisted sequential Monte Carlo scheme. Its twist is a closed-form plug-in surrogate, obtained by combining the velocity and score fields exposed by SB pretraining into an endpoint predictor and evaluating the likelihood there. Since this surrogate is less reliable near the source, we temper the twist with a time-varying exponent, downweighting its influence at early steps. We further introduce a bridge-shaped inference-time diffusion schedule that concentrates the proposal noise in the interior of the path. Telescoping importance weights then cancel all intermediate twist factors, yielding an asymptotically exact sampler for the posterior. Empirically, BridgeTwist outperforms training-free baselines on class-conditional and text-prompted image generation, and is competitive with specialized inverse problem solvers on inpainting, deblurring, and super-resolution.

Figures & tables

Table 4: Text-prompted image generation on CelebA-HQ at K=25K=25 . Subscripts give standard deviations; bold marks the best value.
Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

 InputBridgeTwistGround truth InputBridgeTwistGround truth
4×4\times SR       
Deblurring       
Appendix
Figure 7: CelebA-HQ reconstructions from BridgeTwist for 4×4\times super-resolution (top) and Gaussian deblurring (bottom). Each triplet shows the observation, the BridgeTwist reconstruction, and the ground truth.

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