arXiv:2511.19065v2 Announce Type: replace-cross Abstract: MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear. We analyze the interaction between the two velocities and find: (i) well-established instantaneous velocity is a prerequisite for learning average velocity; (ii) learning of instantaneous velocity benefits from average velocity when the temporal gap is small, but degrades as the gap increases; and (iii) task-affinity analysis indicates that smooth learning o
This research details advancements in generative modeling training, indicating ongoing refinement in foundational AI capabilities.
Improved efficiencies and understanding in generative models can lead to more powerful and accessible AI tools, affecting various industries.
The training dynamics of `MeanFlow` are better understood, potentially enabling faster development of high-quality generative AI applications.
- · AI researchers
- · Generative AI developers
- · Cloud computing providers
- · Content creation industries
- · Inefficient generative model architectures
Further optimization of generative AI models, leading to more practical applications.
Reduced computational costs for certain generative tasks, increasing accessibility for smaller teams.
Acceleration in the pace of AI research and development due to better foundational tools.
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