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arXiv · 2305.14594

torchgfn: A PyTorch GFlowNet library

Joseph D. Viviano·Omar G. Younis·Sanghyeok Choi·Victor Schmidt·Yoshua Bengio·Salem Lahlou
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Abstract

The growing popularity of generative flow networks (GFlowNets or GFNs) from a range of researchers with diverse backgrounds and areas of expertise necessitates a library that facilitates the testing of new features (e.g., training losses and training policies) against standard benchmark implementations, or on a set of common environments. We present torchgfn, a PyTorch library that aims to address this need. Its core contribution is a modular and decoupled architecture which treats environments, neural network modules, and training objectives as interchangeable components. This provides users with a simple yet powerful API to facilitate rapid prototyping and novel research. Multiple examples are provided, replicating and unifying published results. The library is available on GitHub (https://github.com/GFNOrg/torchgfn) and on pypi (https://pypi.org/project/torchgfn/).

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Joseph D. Viviano, Omar G. Younis, Sanghyeok Choi, Victor Schmidt, Yoshua Bengio, Salem Lahlou. 2026-03-20. torchgfn: A PyTorch GFlowNet library. https://arxiv.org/abs/2305.14594

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