diff --git a/README.md b/README.md
index f97713fcd..7aa382bbe 100644
--- a/README.md
+++ b/README.md
@@ -4,9 +4,9 @@ gflownet is library built upon [PyTorch](https://pytorch.org/) to easily train a
 
 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)
+![:scale 25%](docs/images/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 $F_{\theta}(s_t \rightarrow s_{t+1})$.
 
 ![Tetris Environment](docs/images/image.png)
 *Figure 1: The Tetris environment*