fix(smolvla2): default load_vlm_weights=True — don't train from scratch

SmolVLAConfig defaults ``load_vlm_weights=False``. With that and no
``--policy.path``, ``SmolVLMWithExpert.__init__`` builds the VLM via
``SmolVLMForConditionalGeneration(config=...)`` — i.e. a fully
**random-initialised** 500M backbone, including a random ``lm_head``.

For plain SmolVLA that's a deliberate "pre-train the expert" mode.
For SmolVLA2 it's a footgun: the high-level text head *is* the
SmolVLM2 ``lm_head``. Training subtask prediction from a random
language model can only memorise — which is exactly the repetition
collapse seen on the real robot ("the arm the arm the arm …").

SmolVLA2 now defaults ``load_vlm_weights=True`` so every run
fine-tunes the pretrained ``HuggingFaceTB/SmolVLM2-500M-Video-Instruct``
backbone (vision tower + language model + lm_head). The action
expert still trains from scratch on the robot data (standard SmolVLA
fine-tuning); start it from pretrained too by fine-tuning a full
``lerobot/smolvla_base`` checkpoint via ``--policy.path``.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/lerobot/policies/smolvla2/configuration_smolvla2.py

@@ -95,6 +95,25 @@ class SmolVLA2Config(SmolVLAConfig):
     effectively reduces SmolVLA2 back to SmolVLA's flow-only training,
     which is occasionally useful for ablations."""
 
+    load_vlm_weights: bool = True
+    """Load the pretrained SmolVLM2 backbone weights (vision tower +
+    language model + ``lm_head``) instead of random-initialising them.
+
+    ``SmolVLAConfig`` defaults this to ``False`` because the original
+    SmolVLA pre-training run trained the VLM body itself. For SmolVLA2
+    that default is a footgun: the text head **is** the SmolVLM2
+    ``lm_head``, and the high-level subtask supervision is hopeless if
+    it starts from a random language model — it can only memorise.
+    SmolVLA2 therefore defaults this to ``True`` so every run fine-tunes
+    from the pretrained ``vlm_model_name`` checkpoint
+    (``HuggingFaceTB/SmolVLM2-500M-Video-Instruct``).
+
+    Note this loads the *VLM backbone* pretrained; the action expert
+    still trains from scratch on the robot data (standard SmolVLA
+    fine-tuning). To also start the action expert from pretrained
+    weights, fine-tune from a full ``lerobot/smolvla_base`` checkpoint
+    via ``--policy.path``."""
+
     # Per-component prompt dropout (Pi0.7 §V.E) ---------------------------
     # At training, randomly drop non-target context messages whose
     # content was substituted from the named recipe binding. Forces