Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
Keywords: Reward fine-tuning, stochastic optimal control, flow matching, diffusion models, RLHF, adjoint method
TL;DR: We introduce a reward fine-tuning framework for diffusion and flow matching models, based on stochastic optimal control (SOC), and Adjoint Matching, a new SOC algorithm.
Abstract: Dynamical generative models that produce samples through an iterative process, such as Flow Matching and denoising diffusion models, have seen widespread use, but there have not been many theoretically-sound methods for improving these models with reward fine-tuning. In this work, we cast reward fine-tuning as stochastic optimal control (SOC). Critically, we prove that a very specific *memoryless* noise schedule must be enforced during fine-tuning, in order to account for the dependency between the noise variable and the generated samples. We also propose a new algorithm named *Adjoint Matching* which outperforms existing SOC algorithms, by casting SOC problems as a regression problem. We find that our approach significantly improves over existing methods for reward fine-tuning, achieving better consistency, realism, and generalization to unseen human preference reward models, while retaining sample diversity.
Primary Area: generative models
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Submission Number: 1370
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