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src/pages/index.mdx

@@ -166,10 +166,10 @@ In our simulated experiments, we used benchmark tasks from the Meta-World enviro
    <Image source={success_rate} altText="Experiments" />
   </Figure>
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-<Figure caption="Visualization of policy learning in the Meta-World simulation environment. We run PROGRESSOR and several baselines on
-six diverse tasks of various difficulties. We also run PROGRESSOR without online push-back as an ablation. We report the environment
+<Figure caption="Visualization of policy learning in the Meta-World simulation environment. We run P<span style="font-size: 80%">ROGRESSOR</span> and several baselines on
+six diverse tasks of various difficulties. We also run P<span style="font-size: 80%">ROGRESSOR</span> without online push-back as an ablation. We report the environment
 reward during training (left) and the task success rate from 10 rollouts (right) averaged over five seeds. The solid line denotes the mean and
-the transparent area denotes standard deviation. PROGRESSOR demonstrates clear advantages in both metrics, especially at early stages of training">
+the transparent area denotes standard deviation. P<span style="font-size: 80%">ROGRESSOR</span> demonstrates clear advantages in both metrics, especially at early stages of training">
 
 <Image source={legend} altText="legend" />
 </Figure>