torchgfn: A PyTorch GFlowNet library

Understand

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.