Update app/src/content/chapters/folding/09-learnings.mdx

app/src/content/chapters/folding/09-learnings.mdx

@@ -3,11 +3,7 @@ import Sidenote from "../../../components/Sidenote.astro";
 
 ## Learnings
 
-Running all these experiments taught us a lot some expected, some not. Here's what stuck.
-
-### What mattered most
-
-Beyond the experiment findings above, several practical insights stood out:
+We also learned a lot from running all these experiments. Several practical insights stood out:
 
 - **Train a reward model.** [SARM](https://huggingface.co/docs/lerobot/sarm) gave us data scoring, advantage conditioning, and curation in one package. We recommend it even for tasks where you think manual filtering would suffice.
 - **Invest in recording quality early.** More time upfront on clean, consistent recordings pays off more than extra volume.
@@ -16,7 +12,7 @@ Beyond the experiment findings above, several practical insights stood out:
 
 ### For the community: the order of operations
 
-If you're training a policy for a new manipulation task with LeRobot, here's the sequence we'd recommend based on what we learned:
+If you're training a policy for a new manipulation task with LeRobot, here's what we recommend:
 
 1. **Define your task protocol first.** Before collecting a single episode, agree on exactly how the task should be performed. 
 2. **Collect 50–100 clean demonstrations.** Quality over volume. Consistent technique, good camera angles, deliberate motions. This is your foundation, everything else builds on it.