tgritsaev/gflownet-tlm
The source code for the paper "Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization" (ICLR 2025)
This project offers a new method to train Generative Flow Networks (GFlowNets), which are AI models that learn to create new designs, molecules, or bit sequences based on a desired reward. It improves how these models learn by optimizing a 'backward policy' which helps deconstruct and refine the generated items. Researchers and machine learning engineers working on generative AI for design or scientific discovery will find this useful.
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Use this if you are a researcher or machine learning engineer developing or experimenting with GFlowNets and need to improve their convergence speed and ability to discover diverse high-reward solutions.
Not ideal if you are looking for a ready-to-use application for generative design rather than an experimental framework for GFlowNet algorithm development.
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Python
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MIT
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Last pushed
Mar 02, 2025
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