arXiv:2605.12951v2 Announce Type: replace-cross Abstract: We propose Coreset-Induced Conditional Velocity Flow Matching (CCVFM), a generative model that augments hierarchical rectified flow with a data-informed source distribution. Hierarchical flow matching models the full conditional velocity law in velocity space, but its inner flow is asked to transport isotropic Gaussian noise to a multimodal target velocity distribution from scratch. Our key observation is that this inner source can be replaced by a closed-form surrogate built from a coreset of the target. CCVFM first compresses the targ
The paper demonstrates advancements in generative AI by improving velocity flow matching, a technique for generating complex data efficiently, at a time when computational efficiency is becoming critical for AI model scaling.
This research provides a more efficient and potentially more stable method for training generative models, which could accelerate the development of advanced AI applications across various domains, offering significant compute savings.
The proposed 'Coreset-Induced Conditional Velocity Flow Matching' method changes how generative models learn complex data distributions, potentially leading to faster training times and more robust model performance by reducing the reliance on isotropic Gaussian noise.
- · AI research institutions
- · Generative AI developers
- · Cloud computing providers (reduced egress costs)
- · Pharmaceuticals (for drug discovery)
- · Organizations relying on less efficient generative model architectures
More efficient and powerful generative models become accessible for a wider range of applications.
This could lead to a proliferation of highly realistic synthetic data generation and AI-driven creative tools.
Reduced computational costs for training advanced AI models lessens the energy and hardware burden, potentially expanding access to cutting-edge AI development beyond large tech incumbents.
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Read at arXiv cs.LG