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 gflownet is library upon [PyTorch](https://pytorch.org/) to easily train and extend [GFlowNets](https://arxiv.org/abs/2106.04399), also known as GFN or generative flow networks. GFlowNets are a machine learning framework for probabilistic and generative modelling, with a wide range of applications, especially in [scientific discovery](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00002h) problems.
 
-In a nutshell, GFlowNets can be regarded as a generative model designed to sample objects $$x \in \mathcal{X}$$ proportionally to a reward function $$R(x)$$. This results in the potential of sampling diverse objects with high rewards. For example, given the reward landscape depicted below, defined over a two-dimensional space, a well-trained GFlowNet will be able to sample from the four high-reward corners with high probability.
+In a nutshell, GFlowNets can be regarded as a generative model designed to sample objects $x \in \mathcal{X}$ proportionally to a reward function $R(x)$. This results in the potential of sampling diverse objects with high rewards. For example, given the reward landscape depicted below, defined over a two-dimensional space, a well-trained GFlowNet will be able to sample from the four high-reward corners with high probability.
 
 ![:scale 25%](../assets/images/slides/gflownet/reward_landscape.png)
 
-GFlowNets rely on the principle of **compositionality** to generate samples. A meaningful decomposition of samples $$x$$ into multiple intermediate states $$s_0\rightarrow s_1 \rightarrow \dots \rightarrow x$$ can yield generalisable patterns. These patterns can then be learned by neural networks trained to model the value of transitions $$F_{\theta}(s_t \arrow s_{t+1})$$.
+GFlowNets rely on the principle of **compositionality** to generate samples. A meaningful decomposition of samples $x$ into multiple intermediate states $s_0\rightarrow s_1 \rightarrow \dots \rightarrow x$ can yield generalisable patterns. These patterns can then be learned by neural networks trained to model the value of transitions $F_{\theta}(s_t \arrow s_{t+1})$.
 
 ![Tetris Environment](docs/images/image.png)
 *Figure 1: The Tetris environment*