fix(smolvla2): per-recipe inference prompts to match training shape

The four high-level steps shared one generic
``_control_context_messages`` that jammed task + plan + memory +
completed_subtask into a single user message. The recipes in
``smolvla2_hirobot.yaml`` each have a *specific* multi-message layout
(``memory_update``: ``user(task) → assistant(prev memory) →
user(completed subtask)``; ``high_level_subtask``: ``user(task+plan+
memory) → user(current subtask)``; ``user_interjection_response``:
``user(task) → assistant(prev plan) → user(interjection)``). After
``apply_chat_template`` those layouts produce different prompts than
the runtime's flattened single-user-turn version, and the model fell
back to its dominant training mode (VQA JSON output) — generating
``":":":":":":...`` repetition.

Add four per-recipe prompt builders (``_msgs_for_subtask``,
``_msgs_for_memory``, ``_msgs_for_interjection``, ``_msgs_for_vqa``),
each mirroring its sub-recipe's exact message structure including
the ``if_present`` skips. Wire each high-level step to its matching
builder. Inference prompts now line up with what the model saw in
training, so generation should produce coherent text instead of
repeated tokens.

Generic ``_control_context_messages`` is kept (still used by tests
and the no-recipe fallback path).

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

src/lerobot/policies/smolvla2/inference/steps.py

@@ -245,7 +245,15 @@ def _strip_recipe_keys(m: dict[str, Any]) -> dict[str, Any]:
 
 @dataclass
 class HighLevelSubtaskFwd(InferenceStep):
-    """At ~1 Hz, ask the policy for the next subtask."""
+    """At ~1 Hz, ask the policy for the next subtask.
+
+    Mirrors the ``high_level_subtask`` recipe layout exactly:
+
+        user:   "${task}\\nPlan: ${plan}\\nMemory: ${memory}"
+        user:   "Current subtask: ${subtask}"        (if subtask present)
+        ↓ generate ↓
+        assistant: <next subtask>
+    """
 
     policy: Any = None
     observation_provider: Any = None
@@ -258,7 +266,7 @@ class HighLevelSubtaskFwd(InferenceStep):
     def run(self, state: dict[str, Any]) -> dict[str, Any] | None:
         if self.policy is None or not state.get("task"):
             return None
-        ctx = _control_context_messages(state)
+        ctx = _msgs_for_subtask(state)
         observation = _maybe_observation(self.observation_provider)
         msg = _generate_with_policy(
             self.policy, ctx, observation=observation, state=state, label="subtask gen"
@@ -275,7 +283,16 @@ class HighLevelSubtaskFwd(InferenceStep):
 
 @dataclass
 class MemoryUpdateFwd(InferenceStep):
-    """On subtask boundary, refresh the compressed memory."""
+    """On subtask boundary, refresh the compressed memory.
+
+    Mirrors the ``memory_update`` recipe layout exactly:
+
+        user:      "${task}"
+        assistant: "Previous memory: ${prior_memory}"   (if prior memory)
+        user:      "Completed subtask: ${completed_subtask}"  (if subtask)
+        ↓ generate ↓
+        assistant: <new memory>
+    """
 
     policy: Any = None
     observation_provider: Any = None
@@ -285,7 +302,7 @@ class MemoryUpdateFwd(InferenceStep):
         # Don't consume the event — multiple steps may want to react.
         if self.policy is None:
             return None
-        ctx = _control_context_messages(state, include_completed=True)
+        ctx = _msgs_for_memory(state)
         observation = _maybe_observation(self.observation_provider)
         new_memory = _generate_with_policy(
             self.policy, ctx, observation=observation, state=state, label="memory gen"
@@ -297,7 +314,16 @@ class MemoryUpdateFwd(InferenceStep):
 
 @dataclass
 class UserInterjectionFwd(InferenceStep):
-    """On stdin interjection, refresh the plan + emit a paired ``say``."""
+    """On stdin interjection, refresh the plan + emit a paired ``say``.
+
+    Mirrors the ``user_interjection_response`` recipe layout exactly:
+
+        user:      "${task}"
+        assistant: "Previous plan:\\n${prior_plan}"   (if prior plan)
+        user:      "${interjection}"                  (the new utterance)
+        ↓ generate ↓
+        assistant: <plan + <say>...</say>>
+    """
 
     policy: Any = None
     observation_provider: Any = None
@@ -306,10 +332,7 @@ class UserInterjectionFwd(InferenceStep):
     def run(self, state: dict[str, Any]) -> dict[str, Any] | None:
         if self.policy is None or not take_event(state, "user_interjection"):
             return None
-        ctx = _control_context_messages(
-            state,
-            extra_user=state.get("recent_interjection"),
-        )
+        ctx = _msgs_for_interjection(state)
         observation = _maybe_observation(self.observation_provider)
         out = _generate_with_policy(
             self.policy, ctx, observation=observation, state=state, label="plan/say gen"
@@ -340,7 +363,17 @@ class UserInterjectionFwd(InferenceStep):
 
 @dataclass
 class AskVQAFwd(InferenceStep):
-    """On stdin question, answer a frame-grounded VQA."""
+    """On stdin question, answer a frame-grounded VQA.
+
+    Mirrors the ``ask_vqa_*`` recipe layout exactly: a single user
+    turn carrying just the VQA question, plus the camera image block
+    in training (we drop the image at inference because the dataset's
+    image preprocessing doesn't match SmolVLM's vision tower input).
+
+        user:   <question>
+        ↓ generate ↓
+        assistant: <vqa answer>
+    """
 
     policy: Any = None
     observation_provider: Any = None
@@ -352,7 +385,7 @@ class AskVQAFwd(InferenceStep):
         question = state.get("recent_vqa_query")
         if not question:
             return None
-        ctx = _control_context_messages(state, extra_user=question)
+        ctx = _msgs_for_vqa(question)
         observation = _maybe_observation(self.observation_provider)
         answer = _generate_with_policy(
             self.policy, ctx, observation=observation, state=state, label="vqa gen"
@@ -426,6 +459,74 @@ def _control_context_messages(
     return msgs
 
 
+# ---------------------------------------------------------------------------
+# Per-recipe prompt builders. Each one mirrors a single sub-recipe's
+# message layout in ``smolvla2_hirobot.yaml`` so the chat-templated
+# prompt at inference matches what the model saw during training.
+# Generic ``_control_context_messages`` is kept around as a fallback
+# for ad-hoc callers but the four high-level steps now use these.
+# ---------------------------------------------------------------------------
+
+
+def _msgs_for_subtask(state: dict[str, Any]) -> list[dict[str, Any]]:
+    """``high_level_subtask`` recipe layout."""
+    head_parts = [state.get("task") or ""]
+    if state.get("current_plan"):
+        head_parts.append(f"Plan: {state['current_plan']}")
+    if state.get("current_memory"):
+        head_parts.append(f"Memory: {state['current_memory']}")
+    msgs: list[dict[str, Any]] = [
+        {"role": "user", "content": "\n".join(head_parts)}
+    ]
+    if state.get("current_subtask"):
+        msgs.append(
+            {"role": "user", "content": f"Current subtask: {state['current_subtask']}"}
+        )
+    return msgs
+
+
+def _msgs_for_memory(state: dict[str, Any]) -> list[dict[str, Any]]:
+    """``memory_update`` recipe layout."""
+    msgs: list[dict[str, Any]] = [
+        {"role": "user", "content": state.get("task") or ""}
+    ]
+    if state.get("current_memory"):
+        msgs.append(
+            {
+                "role": "assistant",
+                "content": f"Previous memory: {state['current_memory']}",
+            }
+        )
+    if state.get("current_subtask"):
+        msgs.append(
+            {
+                "role": "user",
+                "content": f"Completed subtask: {state['current_subtask']}",
+            }
+        )
+    return msgs
+
+
+def _msgs_for_interjection(state: dict[str, Any]) -> list[dict[str, Any]]:
+    """``user_interjection_response`` recipe layout."""
+    msgs: list[dict[str, Any]] = [
+        {"role": "user", "content": state.get("task") or ""}
+    ]
+    if state.get("current_plan"):
+        msgs.append(
+            {"role": "assistant", "content": f"Previous plan:\n{state['current_plan']}"}
+        )
+    interjection = state.get("recent_interjection")
+    if interjection:
+        msgs.append({"role": "user", "content": interjection})
+    return msgs
+
+
+def _msgs_for_vqa(question: str) -> list[dict[str, Any]]:
+    """``ask_vqa_*`` recipe layout (text-only at inference)."""
+    return [{"role": "user", "content": question}]
+
+
 def _maybe_observation(provider: Any) -> dict | None:
     """Pull one observation from ``provider`` if it's set, else ``None``.