Brief

Researchers have developed a new framework to stabilize and enhance MeanFlow, a technique used for distilling large-scale diffusion models. The method introduces a warm-up phase with a discrete solution before switching to the differential solution for refinement. Additionally, it incorporates trajectory distribution alignment to mitigate "mean-seeking bias" during few-step inference. This approach has demonstrated superior performance when applied to models like FLUX.1-dev and the 80B-parameter HunyuanImage 3.0. AI

Researchers have developed new methods for reinforcement learning policies that aim to improve efficiency and expressiveness. One approach, Score-Based One-step MeanFlow Policy Optimization (SOM), constructs a target velocity field using Q-function scores and a probability flow ODE, enabling state-of-the-art performance in online RL with reduced training and inference times. Another development, Stochastic MeanFlow Policies (SMFP), offers a one-step generative policy class that maps noise to actions through a MeanFlow transformation, providing a unified objective for stable and exploratory policy improvement in off-policy settings. AI