Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

• URL: http://arxiv.org/abs/2409.08861v4
• Date: Sat, 26 Oct 2024 16:28:20 GMT
• Title: Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
• Authors: Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen,
• Abstract summary: We cast reward fine-tuning as optimal control (SOC) for dynamical generative models that produce samples through an iterative process. 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.
• Score: 26.195547996552406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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