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 through a sequence of steps with probability proportional to some reward function (or with denoting the energy function), given at the end of the generative trajectory.
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.
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
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/