diff --git a/src/lerobot/policies/pi052/modeling_pi052.py b/src/lerobot/policies/pi052/modeling_pi052.py
index 5cd56f1f3..b1146ea1a 100644
--- a/src/lerobot/policies/pi052/modeling_pi052.py
+++ b/src/lerobot/policies/pi052/modeling_pi052.py
@@ -851,8 +851,15 @@ class PI052Policy(PI05Policy):
         # ``_prepare_attention_masks_4d`` always returns fp32 (0.0 / -inf
         # literals). When weights are bf16, HF's PaliGemma SDPA raises
         # "invalid dtype for bias - should match query's dtype". Pull the
-        # backbone's dtype once and cast each step's mask to it.
-        backbone_dtype = next(backbone.paligemma.parameters()).dtype
+        # dtype from an attention *projection* weight specifically:
+        # ``to_bfloat16_for_selected_params`` keeps norms / embeddings in
+        # fp32 even when the rest is bf16, so ``next(parameters())``
+        # would land on one of those and we'd skip the cast. q_proj is
+        # always cast with the rest, so its dtype is the one SDPA sees.
+        backbone_dtype = (
+            backbone.paligemma.model.language_model.layers[0]
+            .self_attn.q_proj.weight.dtype
+        )
 
         for _ in range(max_new_tokens):
             att_2d = make_att_2d_masks(current_pad, current_att)