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[2602.12529] Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models
Summary
Flow-Factory presents a unified framework for reinforcement learning in flow-matching models, addressing fragmentation and complexity in existing implementations.
Why It Matters
This framework enhances the efficiency of developing and scaling reinforcement learning applications by providing a modular architecture that simplifies integration and reduces implementation overhead, which is crucial for researchers and practitioners in the rapidly evolving field of machine learning.
Key Takeaways
- Flow-Factory decouples algorithms, models, and rewards, promoting modularity.
- Supports multiple models and algorithms, facilitating rapid prototyping.
- Offers production-ready features like memory optimization and distributed training.
Computer Science > Machine Learning arXiv:2602.12529 (cs) [Submitted on 13 Feb 2026] Title:Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models Authors:Bowen Ping, Chengyou Jia, Minnan Luo, Hangwei Qian, Ivor Tsang View a PDF of the paper titled Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models, by Bowen Ping and 4 other authors View PDF HTML (experimental) 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 this https URL. Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2602.12529 [cs.LG] (or arXiv:2602.12529v1 [cs.LG] for this version) ht...
