diff --git a/src/lerobot/policies/pi05_full/modeling_pi05.py b/src/lerobot/policies/pi05_full/modeling_pi05.py
index 3db999fdf..8a00215f0 100644
--- a/src/lerobot/policies/pi05_full/modeling_pi05.py
+++ b/src/lerobot/policies/pi05_full/modeling_pi05.py
@@ -317,16 +317,16 @@ def compute_layer_complete(
     V_action = value_states[:, :, vlm_len:, :]   # (B, num_kv_heads, action_len, head_dim)
 
     # create detached vlm K/V for action queries
-    # .detach() stops gradient flow: action loss won't backprop into VLM's K/V projections
+    # .detach() stops gradient flow: action loss wont backprop into VLM's K/V projections
     K_vlm_detached = K_vlm.detach()
     V_vlm_detached = V_vlm.detach()
 
     # K/V for VLM queries: use original (full gradient flow for VLM self-attention)
-    K_for_vlm = key_states      # Full concatenated K: [K_vlm, K_action]
-    V_for_vlm = value_states    # Full concatenated V: [V_vlm, V_action]
+    K_for_vlm = key_states  # [K_vlm, K_action]
+    V_for_vlm = value_states  # [V_vlm, V_action]
 
     # K/V for action queries: detached VLM K/V + normal action K/V
-    # This implements the knowledge insulation: action queries can "see" VLM K/V
+    # knowledge insulation: action queries can "see" VLM K/V
     # in forward pass, but gradients are blocked in backward pass
     K_for_action = torch.cat([K_vlm_detached, K_action], dim=2)
     V_for_action = torch.cat([V_vlm_detached, V_action], dim=2)