Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, model-specific implementations, and engineering complexity. We introduce Flow-Factory, a unified framework that decouples algorithms, models, and rewards through through a modular, registry-based architecture. This design enables seamless integration of new algorithms and architectures, as demonstrated by our support for GRPO, DiffusionNFT, and AWM across Flux, Qwen-Image, and WAN video models. By minimizing implementation overhead, Flow-Factory empowers researchers to rapidly prototype and scale future innovations with ease. Flow-Factory provides production-ready memory optimization, flexible multi-reward training, and seamless distributed training support. The codebase is available at https://github.com/X-GenGroup/Flow-Factory.
@article{arxiv.2602.12529,
title = {Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models},
author = {Bowen Ping and Chengyou Jia and Minnan Luo and Hangwei Qian and Ivor Tsang},
journal= {arXiv preprint arXiv:2602.12529},
year = {2026}
}