ArXiv

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

2026-07-16 · 17:59 UTC ·Yushi Huang, Xiangxin Zhou, Jun Zhang... ·1 min read
Authors
Yushi Huang, Xiangxin Zhou, Jun Zhang...
Categories
cs.CV, cs.LG
arXiv
https://arxiv.org/abs/2607.15273v1
PDF
https://arxiv.org/pdf/2607.15273v1

Brief

MeanFlowNFT extends forward-process RL to MeanFlow average-velocity generators by deriving an induced instantaneous-velocity predictor from a MeanFlow identity and applying the DiffusionNFT objective; sampling still uses average velocities for fast few-step generation. The authors prove a policy-improvement guarantee and report state-of-the-art image/video results (e.g., 4-step VBench 84.33 vs 50-step 82.57).

Why it matters

MeanFlowNFT adapts DiffusionNFT's forward-process RL to MeanFlow average-velocity generators by deriving an induced instantaneous-velocity predictor via a MeanFlow identity, enabling reward optimization while preserving MeanFlow's fast few-step (average-velocity) sampling.

Key details

  • The paper proves MeanFlowNFT inherits DiffusionNFT's strict policy-improvement guarantee and reports consistent image/video gains, outperforming prior RL-tuned few-step generators on 6 of 8 metrics on SD3.5-M.
  • Empirical highlight: 4-step MeanFlowNFT on Wan 2.1 achieves VBench 84.33 versus 50-step LongCat-Video RL at 82.57; code and models available (GitHub/Hugging Face), arXiv:2607.15273 (posted 2026-07-16).
Source evidence

Abstract

MeanFlow generators achieve fast few-step sampling by predicting average velocities over time intervals, making them attractive for efficient generation. Reinforcement learning (RL) has become a powerful way to align diffusion and flow models with human preferences and task-specific objectives. In particular, DiffusionNFT offers an efficient forward-process RL framework that does not require reverse-process trajectories or likelihood estimation. However, applying such RL methods to MeanFlow remains underexplored. DiffusionNFT optimizes instantaneous velocities, whereas MeanFlow samples with average velocities. To bridge this gap, we introduce MeanFlowNFT. Inspired by the MeanFlow identity, which bridges average and instantaneous velocities, we construct an induced instantaneous-velocity predictor. We apply the DiffusionNFT objective to this predictor, making reward optimization well-defined for MeanFlow. Sampling remains based on the average velocity, preserving MeanFlow's fast few-step generation. We further prove that MeanFlowNFT inherits DiffusionNFT's strict policy-improvement guarantee. Experiments on image and video generation show that MeanFlowNFT consistently improves baselines. Moreover, it outperforms prior state-of-the-art RL-tuned few-step generators on most metrics ($6$ of $8$ on SD3.5-M), and can even surpass multi-step RL-tuned diffusion while using only a few sampling steps. For instance, on Wan 2.1, $4$-step MeanFlowNFT reaches a VBench score of $84.33$, surpassing $50$-step LongCat-Video RL ($82.57$).

Comment: Project Page: https://harahan.github.io/meanflownft-project-page/, GitHub: https://github.com/Harahan/MeanFlowNFT, Hugging Face: https://huggingface.co/Harahan/MeanFlowNFT