Title: A unified perspective on fine-tuning and sampling with diffusion and flow models
Primary area: generative models
Keywords: diffusion models, flow matching, sampling, reward fine-tuning, neural sampler, stochastic optimal control, thermodynamics, Jarzynski identity, Crooks fluctuation theorem

Abstract: We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of the base density. This tasks subsumes sampling from unnormalized densities and reward fine-tuning a pre-trained model, and can be approached from a stochastic optimal control (SOC) perspective and from a thermodynamics perspective. The SOC formulation has been tackled using adjoint-based methods (Adjoint Matching and Sampling), and score matching methods, while the thermodynamics formulation has given rise to algorithms such as CMCD and NETS. Our contributions include bounding the lean adjoint ODE underlying Adjoint Matching and Sampling, deriving bias–variance decompositions that allow a principled comparison between adjoint-based and score-matching methods, adapting thermodynamic formulations to the exponential tilting setting, and text-to-image fine-tuning experiments.

TL;DR: We unify SOC- and thermodynamics-based approaches to fine-tuning and sampling with diffusion and flow models