Training Flow-based Generative Models with Adaptive Curriculum Sampler

The training of state-of-the-art flow-based generative models can be viewed as learning to denoise latents across a continuum of timesteps, or noise levels. Despite their empirical success, the role of timestep sampling in training and the relationship between flow-based generative models and conventional denoising methods remain insufficiently understood in existing literature. To answer this question, in this paper we present a principled view that interprets noise level as a proxy for sample difficulty in curriculum learning from a denoising perspective, thereby providing a natural basis for organizing samples with different noise levels into an easy-to-hard curricula. We show both theoretically and empirically that noise level provides a consistent measure for this curriculum-level difficulty, establishing it as a foundation for guiding the sampling distribution. This perspective further suggests that the timestep sampling should adapt to the model’s evolving denoising ability during training. However, existing methods typically rely on entirely fixed or weakly adaptive strategies, limiting the generation performance. To address this issue, we further propose CurFM}, a Curriculum-based adaptive sampler for flow-based generative models that progressively shifts the timestep distribution toward more challenging noise levels according to the model’s current denoising capability, without relying on unstable loss signals. CurFM benefits in minimal modifications to existing flow-based training pipelines without changes during inference, while achieving faster convergence as well as competitive or superior performance under both v- and $ϵ$-prediction frameworks.