Meta Flow Maps

Controlling generative models—whether via inference-time steering or fine-tuning—is expensive. Control relies on estimating the value function—typically necessitating costly trajectory simulations. To eliminate this bottleneck, we introduce Meta Flow Maps (MFMs), stochastic extensions of consistency models and flow maps. MFMs are trained to perform one-step posterior sampling, generating arbitrarily many i.i.d. draws of clean data x_1 from any noisy state x_t. Crucially, these samples are differentiable in the conditioning state x_t, unlocking efficient estimation of the value function gradient. We leverage this capability to enable both inference-time steering without inner rollouts, and unbiased, off-policy fine-tuning to general rewards. Among our fine-tuning and steering experiments on ImageNet, we highlight that our single-particle steered-MFM sampler outperforms a Best-of-1000 baseline across multiple rewards at a fraction of the compute. Speaker Bio: Peter Potaptchik is a PhD student at Oxford advised by Yee Whye Teh, and a visiting fellow at Harvard advised by Michael S. Albergo. Find seminar details and upcoming talks: https://www.microsoft.com/en-us/research/event/microsoft-research-new-england-generative-modeling-sampling-seminar/