arXiv:2603.10395v2 Announce Type: replace Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, \aka, graph flow model (GFM), has emerged due to its superior performance and flexible sampling. However, effectively aligning GFMs with complex human preferences or task-specific objectives remains a significant challenge. In this paper, we propose Graph-GRPO, an online reinforcement learning (RL) framework for training GFMs under verifiable rewards. Our method makes two key contributions: (1) We de
The increasing complexity of graph generation tasks and the need for explainable and controllable AI lead to novel methods like Graph-GRPO at the intersection of flow models and reinforcement learning.
This development offers a significant step towards generating complex molecular structures and materials efficiently, directly impacting drug discovery and advanced materials science.
The ability to train graph flow models with verifiable rewards using reinforcement learning means more effective and aligned graph generation for intricate tasks, potentially accelerating discovery processes.
- · Pharmaceutical companies
- · Materials science
- · AI researchers (Graph ML)
- · Biotechnology sector
- · Traditional drug discovery methods
- · Less efficient graph generation techniques
Accelerated discovery of novel drug candidates and materials with desired properties.
Reduced R&D costs and shortened time-to-market for new products in pharmaceuticals and advanced materials.
Potential for AI-driven design of entire molecular libraries or biological systems, rather than individual compounds.
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Read at arXiv cs.LG