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.

翻译：流匹配与扩散模型在推理时通过奖励对齐算法可大幅提升性能，但效率仍是主要限制因素。尽管已有多种算法被提出，我们证明这些算法共有的瓶颈在于其依赖的采样方法：多数算法需要通过随机微分方程采样来获取马尔可夫转移样本，其效率显著低于常微分方程采样且性能通常较差。为消除此瓶颈，我们提出GLASS Flows——一种通过模拟“流匹配模型中的流匹配模型”来采样马尔可夫转移的全新采样范式。本研究表明，这种“内部”流匹配模型无需重新训练即可从预训练模型中提取，从而将常微分方程的效率与随机微分方程的随机演化特性相结合。在大规模文生图模型上，GLASS Flows消除了随机演化与效率之间的权衡。结合Feynman-Kac引导方法，GLASS Flows在文生图生成任务中实现了最先进的性能，为流模型与扩散模型的推理时缩放提供了即插即用的简洁解决方案。