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arXiv · 2602.12529

Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models

Bowen Ping·Chengyou Jia·Minnan Luo·Hangwei Qian·Ivor Tsang
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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. The codebase is available at https://github.com/X-GenGroup/Flow-Factory.

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Bowen Ping, Chengyou Jia, Minnan Luo, Hangwei Qian, Ivor Tsang. 2026-02-13. Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models. https://arxiv.org/abs/2602.12529

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