Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910198906486784 |
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| author | Guo, Zhengyi Sheng, Jiayuan Yao, David D. Tang, Wenpin |
| author_facet | Guo, Zhengyi Sheng, Jiayuan Yao, David D. Tang, Wenpin |
| contents | We propose a deterministic adjoint matching framework that formulates human preference alignment for flow-based generative models as an optimal control problem over velocity fields. One can directly regress the control toward a value-gradient-induced target under the current policy, leading to a simple and stable training objective. Building on this perspective, we introduce a truncated adjoint scheme that focuses computation on the terminal portion of the trajectory, where reward-relevant signals concentrate, which yields substantial computational savings while preserving alignment quality. We further generalize the framework beyond standard KL-based regularization, allowing more flexible trade-offs between alignment strength and distributional preservation. Experiments on SiT-XL/2 and FLUX.2-Klein-4B demonstrate consistent gains across multiple alignment metrics, along with substantially improved diversity and mode preservation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06583 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline Guo, Zhengyi Sheng, Jiayuan Yao, David D. Tang, Wenpin Artificial Intelligence We propose a deterministic adjoint matching framework that formulates human preference alignment for flow-based generative models as an optimal control problem over velocity fields. One can directly regress the control toward a value-gradient-induced target under the current policy, leading to a simple and stable training objective. Building on this perspective, we introduce a truncated adjoint scheme that focuses computation on the terminal portion of the trajectory, where reward-relevant signals concentrate, which yields substantial computational savings while preserving alignment quality. We further generalize the framework beyond standard KL-based regularization, allowing more flexible trade-offs between alignment strength and distributional preservation. Experiments on SiT-XL/2 and FLUX.2-Klein-4B demonstrate consistent gains across multiple alignment metrics, along with substantially improved diversity and mode preservation. |
| title | Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.06583 |