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arXiv · 2610.20035

Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost

Eliot Beyler·Francis Bach
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Abstract

We introduce Weighted Adjoint Matching, a method to finetune a pretrained diffusion or flow matching model given a differentiable reward. We derive it from Adjoint Matching, which reformulates finetuning as a stochastic optimal control problem with a quadratic control cost. Building on the observation that these models generate coarse features before fine details, we place a time-dependent weight on the control cost. A high cost early in the generative process preserves balance and diversity in the coarse features, which carry most of the semantics, while a low cost at the end lets the model increase the reward by refining details. We prove that our method is equivalent to maximizing the reward under time-varying Kullback--Leibler regularization on the generative paths. Experiments on Gaussian mixtures and on MNIST show a better tradeoff between reward and mode balance than Adjoint Matching.

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Eliot Beyler, Francis Bach. 2026-10-14. Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost. https://arxiv.org/abs/2610.20035

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