The cost of short-video models is being reshaped by the "few-step generation" route. DiffusionNFT uses the forward-process negative likelihood estimate for RL post-training, avoiding the high cost of reverse trajectories and likelihood estimates, but it was originally designed for "instantaneous-velocity" diffusion models; MeanFlow, the "average-velocity" generator, goes a different way — it predicts the average velocity over a time interval, compressing sampling steps to 4 or even 1. The two formulas differ, and the RL optimization target had no way to connect. Tencent Hunyuan's MeanFlowNFT (arXiv 2607.15273), released July 16, fills this gap. The team uses the MeanFlow identity to build a bridge between "average velocity" and "instantaneous velocity": in training, an induced instantaneous-velocity predictor is constructed and the DiffusionNFT objective is applied; at sampling, the model still runs only average-velocity, so the few-step generation advantage is preserved to the letter. The paper also proves MeanFlowNFT inherits DiffusionNFT's policy-monotonic-improvement guarantee. The numbers are most persuasive: on the Wan 2.1 video model, 4-step MeanFlowNFT pushes VBench to 84.33, directly beating 50-step LongCat-Video RL (82.57); on SD3.5-M image generation, it beats all previous RL-tuned few-step generators in 6 of 8 evaluation dimensions. The project is open-sourced (github.com/Harahan/MeanFlowNFT). Changing the RL post-training cost curve from "must be paired with a multi-step ODE solver" to "few steps can stably align rewards" is a direct boon to real-time video generation, interactive video Agents, and the like. This work deserves attention from practitioners beyond Hunyuan: it's not a single-model upgrade, but a course correction for the entire few-step generation route, completing the RL chapter.