Generative Flow Networks or GFlowNets is a new paradigm of neural net training, developed at MILA since 2021.

GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an energy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood). GFlowNet are trained to generate an object $x$ through a sequence of steps with probability proportional to some reward function $R\left(x\right)$ (or $e^{-E\left(x\right)}$ with $E\left(x\right)$ denoting the energy function), given at the end of the generative trajectory.^[1]

Through generative models and variational inference, GFlowNets are also related to Active Inference.

GFlowNets promise better interpretability and more robust reasoning than the current auto-regressive LLMs^[2].

1. ^

   Pan, L., Malkin, N., Zhang, D., & Bengio, Y. (2023). Better Training of GFlowNets with Local Credit and Incomplete Trajectories (arXiv:2302.01687). arXiv. https://​​doi.org/​​10.48550/​​arXiv.2302.01687

2. ^

   Bengio, Y., & Hu, E. (2023, March 21). Scaling in the service of reasoning & model-based ML. Yoshua Bengio. https://​​yoshuabengio.org/​​2023/​​03/​​21/​​scaling-in-the-service-of-reasoning-model-based-ml/​​