diff --git a/README.md b/README.md
index a867e2e1..41a04997 100644
--- a/README.md
+++ b/README.md
@@ -152,8 +152,9 @@ See `python lerobot/scripts/eval.py --help` for more instructions.
 
 Check out [example 3](./examples/3_train_policy.py) to see how you can start training a model on a dataset, which will be automatically downloaded if needed.
 
-In general, you can use our training script to easily train any policy in any environment:
+In general, you can use our training script to easily train any policy on its environment:
 ```bash
+# TODO(aliberts): not working
 python lerobot/scripts/train.py \
     env=aloha \
     task=sim_insertion \
diff --git a/examples/3_train_policy.py b/examples/3_train_policy.py
index 134271ea..2b6c3c6a 100644
--- a/examples/3_train_policy.py
+++ b/examples/3_train_policy.py
@@ -18,7 +18,7 @@ from lerobot.common.utils.utils import init_hydra_config
 output_directory = Path("outputs/train/example_pusht_diffusion")
 os.makedirs(output_directory, exist_ok=True)
 
-# Number of offline training steps (we'll only do offline training for this example.
+# Number of offline training steps (we'll only do offline training for this example.)
 # Adjust as you prefer. 5000 steps are needed to get something worth evaluating.
 training_steps = 5000
 device = torch.device("cuda")