arXiv:2505.20110v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) excel at sampling diverse, high-reward objects. In many practical applications where active reward queries are infeasible, these models must be trained using static offline datasets. Prevailing training methods typically rely on a proxy model to provide reward feedback for online sampled trajectories. However, constructing a reliable proxy is often challenging due to data scarcity or high evaluation costs. While existing proxy-free approaches attempt to address this, they often impose coarse constraints th
This research addresses a fundamental challenge in training advanced generative AI models with limited data, a common and pressing issue in real-world applications.
Improving offline training for GFlowNets can accelerate AI development in fields where data acquisition is costly or scarce, broadening their applicability and effectiveness.
The ability to train GFlowNets effectively without relying on expensive or unreliable proxy models for reward feedback significantly lowers the barrier to entry and deployment.
- · AI researchers
- · Machine learning startups
- · Data-scarce industries
- · Generative AI applications
- · Companies reliant on large, clean datasets
- · Proxy model developers
- · Expensive data collection services
More robust and efficient training of generative AI models, particularly GFlowNets, in scenarios with limited active querying capacity.
Accelerated deployment of advanced AI applications in sectors like drug discovery, material science, and personalized medicine, where data is inherently sparse.
Enhanced overall AI capabilities due to the ability to leverage existing, imperfect datasets more effectively, reducing development costs and time.
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Read at arXiv cs.LG