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MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators

Research Multimodal & Generative

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Overall 80
Content 85
Popularity 68

Observed public metrics from 1 member.

Merged summary

TL;DR - MeanFlowNFT adapts DiffusionNFT's forward-process reinforcement learning to MeanFlow average-velocity generators, enabling reward optimization while preserving fast few-step sampling for image and video generation.

  • Bridges the mismatch between DiffusionNFT (optimizes instantaneous velocities) and MeanFlow (samples average velocities) by using the MeanFlow identity to build an induced instantaneous-velocity predictor, then applying the DiffusionNFT objective to it.
  • Sampling still uses average velocity, preserving MeanFlow's few-step efficiency; the method provably inherits DiffusionNFT's strict policy-improvement guarantee.
  • Outperforms prior SOTA RL-tuned few-step generators on most metrics (6 of 8 on SD3.5-M) and can beat multi-step RL-tuned diffusion using only a few steps.
  • On Wan 2.1, 4-step MeanFlowNFT reaches a VBench score of 84.33, surpassing 50-step LongCat-Video RL (82.57).

Sources (1)

MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators

arXiv cs.CV Yushi Huang, Xiangxin Zhou, Jun Zhang, Liefeng Bo, Tianyu Pang 2026-07-16 arXiv:2607.15273
Public signals Hugging Face upvotes 17
Providers: Hugging Face · Upvotes 17 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-15 14:33:54.774716 UTC

TL;DR - MeanFlowNFT adapts DiffusionNFT's forward-process reinforcement learning to MeanFlow average-velocity generators, enabling reward optimization while preserving fast few-step sampling for image and video generation.

  • Bridges the mismatch between DiffusionNFT (optimizes instantaneous velocities) and MeanFlow (samples average velocities) by using the MeanFlow identity to build an induced instantaneous-velocity predictor, then applying the DiffusionNFT objective to it.
  • Sampling still uses average velocity, preserving MeanFlow's few-step efficiency; the method provably inherits DiffusionNFT's strict policy-improvement guarantee.
  • Outperforms prior SOTA RL-tuned few-step generators on most metrics (6 of 8 on SD3.5-M) and can beat multi-step RL-tuned diffusion using only a few steps.
  • On Wan 2.1, 4-step MeanFlowNFT reaches a VBench score of 84.33, surpassing 50-step LongCat-Video RL (82.57).
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