GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models

• URL: http://arxiv.org/abs/2509.25170v1
• Date: Mon, 29 Sep 2025 17:58:36 GMT
• Title: GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
• Authors: Peter Holderrieth, Uriel Singer, Tommi Jaakkola, Ricky T. Q. Chen, Yaron Lipman, Brian Karrer,
• Abstract summary: We introduce GLASS Flows, a new sampling paradigm that simulates a "flow matching model" to sample Markov transitions.<n>On large-scale text-to-image models, we show that GLASS Flows eliminate the trade-off between evolution and efficiency.
• Score: 42.15046750300825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of flow matching and diffusion models can be greatly improved at inference time using reward alignment 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 a pre-trained model without any re-training, 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. Combined with Feynman-Kac Steering, 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.

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