update doc

docs/source/libero.mdx

@@ -79,18 +79,23 @@ When using LIBERO through LeRobot, policies interact with the environment via **
 We also provide a notebook for quick testing:
 Training with LIBERO
 
+## Training with LIBERO
+
 When training on LIBERO tasks, make sure your dataset parquet and metadata keys follow the LeRobot convention.
+
 The environment expects:
 
-observation.state → 8-dim agent state
+- `observation.state` → 8-dim agent state
+- `observation.images.image` → main camera (`agentview_image`)
+- `observation.images.image2` → wrist camera (`robot0_eye_in_hand_image`)
 
-observation.images.image → main camera (agentview_image)
+⚠️ Cleaning the dataset upfront is **cleaner and more efficient** than remapping keys inside the code. We plan to provide a script to easily preprocess such data.
 
-observation.images.image2 → wrist camera (robot0_eye_in_hand_image)
+---
 
-⚠️ Cleaning the dataset upfront is cleaner and more efficient than remapping keys inside the code. We plan to provide a script to easily preprocess such data.
+### Example training command
 
-Example training command
+```bash
 python src/lerobot/scripts/train.py \
   --policy.type=smolvla \
   --dataset.repo_id=jadechoghari/smol-libero3 \
@@ -102,11 +107,13 @@ python src/lerobot/scripts/train.py \
   --env.multitask_eval=True \
   --eval.batch_size=1 \
   --eval.n_episodes=1
+```
 
-Note on rendering
+---
+
+### Note on rendering
 
 LeRobot uses MuJoCo for simulation. You need to set the rendering backend before training or evaluation:
 
-export MUJOCO_GL=egl → for headless servers (e.g. HPC, cloud)
-
-export MUJOCO_GL=glfw → for local runs with a display
+- `export MUJOCO_GL=egl` → for headless servers (e.g. HPC, cloud)
+- `export MUJOCO_GL=glfw` → for local runs with a display