Flow-Factory: A unified framework for easy reinforcement learning in Flow-Matching models

Bowen Ping, Chengyou Jia, Minnan Luo, Hangwei Qian, Ivor Tsang

Keywords: Flow-Matching, Reinforcement Learning

Abstract:

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.

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Submission Number: 66