diff --git a/docs/source/features/algorithms/VROOM/VROOM.png b/docs/source/features/algorithms/VROOM/VROOM.png
new file mode 100644
index 0000000..56c287b
Binary files /dev/null and b/docs/source/features/algorithms/VROOM/VROOM.png differ
diff --git a/docs/source/features/algorithms/VROOM/VROOM.rst b/docs/source/features/algorithms/VROOM/VROOM.rst
index a0668d2..51cd9fb 100644
--- a/docs/source/features/algorithms/VROOM/VROOM.rst
+++ b/docs/source/features/algorithms/VROOM/VROOM.rst
@@ -5,26 +5,27 @@ Introduction
 ------------
 `paper <https://proceedings.mlr.press/v108/ammar20a/ammar20a.pdf>`_, `code <https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/VROOM.py>`_
 
-In this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret,
+**Title:** Derivative-Free & Order-Robust Optimisation
+
+**Authors:** Victor Gabillon, Rasul Tutunov, Michal Valko, Haitham Bou Ammar
+
+**Abstract:** In this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret,
 and propose VROOM, a zeroth order optimisation algorithm capable of achieving vanishing regret in non-stationary
 environments, while recovering favorable rates under stochastic reward-generating processes. Our results are the first
 to target simple regret definitions in adversarial scenarios unveiling a challenge that has been rarely considered
 in prior work.
 
+.. image:: VROOM.png
+
+
 Algorithm Parameters
 --------------------
-    * n: int
-        The total number of rounds (budget)
-    * h_max: int
-        The maximum depth of the partition
-    * b: float
-        The parameter that measures the variation of the function
-    * f_max: float
-        An upper bound of the objective function
-    * domain: list(list)
-        The domain of the objective to be optimized
-    * partition:
-        The partition choice of the algorithm
+    * `n: int`- The total number of rounds (budget)
+    * `h_max: int` - The maximum depth of the partition
+    * `b: float` - The parameter that measures the variation of the function
+    * `f_max: float` - An upper bound of the objective function
+    * `domain: list(list)`- The domain of the objective to be optimized
+    * `partition` - The partition choice of the algorithm
 
 Usage Example
 -------------