Mean Flows for One-step Generative Modeling — interactive visual explainer | Rudrite Research

Geng et al. published a paper on arXiv 2025 introducing Mean Flows for one-step generative modeling, which trains on average velocity instead of instantaneous velocity to enable one-step generation from scratch without distillation. An interactive visual explainer of the paper is available online.

Mean Flows for One-step Generative Modeling Train on average velocity, not instantaneous — one-step generation from scratch, no distillation. Geng et al. · arXiv 2025 · Foundations. Read the paper ↗ https://arxiv.org/abs/2505.13447 A free, interactive, animated visual explainer of Mean Flows for One-step Generative Modeling — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is Mean Flows for One-step Generative Modeling? - Train on average velocity, not instantaneous — one-step generation from scratch, no distillation. - Who published Mean Flows for One-step Generative Modeling, and where? - Geng et al. — arXiv 2025 arXiv:2505.13447 . - Where can I find a visual explainer of Mean Flows for One-step Generative Modeling? - Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source. Related explainers Attention Is All You Need /attention GPT-3: Language Models are Few-Shot Learners /gpt-3 Mixtral of Experts /mixtral Training Compute-Optimal Large Language Models /chinchilla Mamba: Linear-Time Sequence Modeling with Selective State Spaces /mamba BERT: Pre-training of Deep Bidirectional Transformers /bert Scaling Laws for Neural Language Models /scaling-laws Adam: A Method for Stochastic Optimization /adam