diff --git a/units/en/unit3/hands-on.mdx b/units/en/unit3/hands-on.mdx
index e9c07cf..1d64f14 100644
--- a/units/en/unit3/hands-on.mdx
+++ b/units/en/unit3/hands-on.mdx
@@ -18,7 +18,7 @@ We're using the [RL-Baselines-3 Zoo integration](https://github.com/DLR-RM/rl-ba
 
 Also, **if you want to learn to implement Deep Q-Learning by yourself after this hands-on**, you definitely should look at CleanRL implementation: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py
 
-To validate this hands-on for the certification process, you need to push your trained model to the Hub and **get a result of >= 500**.
+To validate this hands-on for the certification process, you need to push your trained model to the Hub and **get a result of >= 200**.
 
 To find your result, go to the leaderboard and find your model, **the result = mean_reward - std of reward**
 
@@ -68,13 +68,6 @@ Before diving into the notebook, you need to:
 We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues).
 
 # Let's train a Deep Q-Learning agent playing Atari' Space Invaders 👾 and upload it to the Hub.
-
-To validate this hands-on for the certification process, you need to push your trained model to the Hub and **get a result of >= 500**.
-
-To find your result, go to the leaderboard and find your model, **the result = mean_reward - std of reward**
-
-For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
-
 ## Set the GPU 💪
 
 - To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type`