Title: GLASS Flows: Efficient Inference for Reward Alignment of Flow and Diffusion Models
Primary area: generative models
Keywords: Flow Matching; Diffusion Models; Reward Alignment; Reward Adaptation; Inference-time scaling; Feynman-Kac Steering; Markov transitions; Sampling methods

Abstract: The performance of flow matching and diffusion models can be greatly improved at inference time using reward adaptation algorithms, yet efficiency remains a major limitation. While several algorithms were proposed, we demonstrate that a common bottleneck is the *sampling* method these algorithms rely on: many algorithms require to sample Markov transitions via SDE sampling, which is significantly less efficient and often less performant than ODE sampling. To remove this bottleneck, we introduce GLASS Flows, a new sampling paradigm that simulates a ''flow matching model within a flow matching model'' to sample Markov transitions. As we show in this work, this ''inner'' flow matching model can be retrieved from any pre-trained model without any re-training, effectively combining the efficiency of ODEs with the stochastic evolution of SDEs. On large-scale text-to-image models, we show that GLASS Flows eliminate the trade-off between stochastic evolution and efficiency. GLASS Flows improve state-of-the-art performance in text-to-image generation, making it a simple, drop-in solution for inference-time scaling of flow and diffusion models.

TL;DR: We improve inference-time reward alignment of flow matching and diffusion models by proposing a novel sampling paradigm that enables more efficient exploration.