diff --git a/examples/rtc/eval_dataset.py b/examples/rtc/eval_dataset.py
index bacafead1..5ec8adf8a 100644
--- a/examples/rtc/eval_dataset.py
+++ b/examples/rtc/eval_dataset.py
@@ -417,7 +417,7 @@ class RTCEvaluator:
     def run_evaluation(self):
         """Run evaluation on two random dataset samples using three separate policies.
 
-        Note: Policies are deinitalized after each step to free memory. Large models
+        Note: Policies are deinitialized after each step to free memory. Large models
         (e.g., VLA models with billions of parameters) cannot fit three instances in
         memory simultaneously. By deleting and garbage collecting after each step,
         we ensure only one policy is loaded at a time.
diff --git a/src/lerobot/policies/molmoact2/molmoact2_hf_model/modeling_molmoact2.py b/src/lerobot/policies/molmoact2/molmoact2_hf_model/modeling_molmoact2.py
index e2edbe68d..bdd030c0f 100644
--- a/src/lerobot/policies/molmoact2/molmoact2_hf_model/modeling_molmoact2.py
+++ b/src/lerobot/policies/molmoact2/molmoact2_hf_model/modeling_molmoact2.py
@@ -1801,7 +1801,7 @@ class MolmoAct2VisionBackbone(nn.Module):
         valid = pooled_patches_idx >= 0
         valid_token = torch.any(valid, -1)
 
-        # Use `pooled_patches_idx` to arange the features for image pooling
+        # Use `pooled_patches_idx` to arrange the features for image pooling
         batch_idx = torch.arange(
             pooled_patches_idx.shape[0],
             dtype=torch.long,