arXiv:2606.15793v1 Announce Type: cross Abstract: This paper explores policy gradient algorithms for training stochastic policies to sample from structured discrete probability distributions under the Generative Flow Network (GFlowNet) framework. Building on extensive theoretical connections between GFlowNets and entropy-regularized reinforcement learning, we derive equivalents of standard policy gradient algorithms for training GFlowNets, as well as experimentally explore their various methodological aspects, including baseline training and advantage estimation. Most importantly, our work is
This research builds on existing theoretical connections in Generative Flow Networks (GFlowNets) and reinforces the ongoing development in AI sampling methods, which is a rapidly evolving field.
Improved discrete sampling techniques are crucial for advancing generative AI models, which have broad applications from drug discovery to materials science and complex system simulation.
The application of Proximal Policy Optimization (PPO) to GFlowNets offers a more robust and efficient way to train policies for sampling from complex distributions, potentially accelerating discovery and design in various domains.
- · AI researchers
- · Deep learning practitioners
- · Pharmaceutical industry
- · Material science
- · Traditional sampling methods
- · Computational chemistry (less efficient methods)
More efficient training of generative models for complex data distributions.
Faster and more accurate discovery of novel molecules, materials, or designs through improved generative capabilities.
Enhanced AI systems capable of autonomously exploring vast design spaces, leading to breakthroughs in diverse scientific and engineering fields.
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Read at arXiv cs.AI