feat(smolvla2-runtime): --text_min_new_tokens / --text_temperature CLI debug knobs

The recipe fix (target=${subtask} instead of ${next_subtask}) shifted
the LM head's failure mode from "emit newlines" to "emit EOS at
position 0". On the new ``_tool-good`` checkpoint inference produces
exactly one token (``<end_of_utterance>``, id 49279) and decodes to
empty. That's the chat-pretrained backbone's short-turn EOS prior
not yet being overridden by 2000 steps of fine-tuning supervision.

Expose three knobs so the operator can probe whether the head has
real subtask-token probability mass *under* the EOS argmax without
recompiling or retraining:

  --text_min_new_tokens=N    suppress EOS for the first N tokens
  --text_temperature=T       sample at temperature T
  --text_top_p=P             nucleus filtering at top-p

These are explicitly off-policy (training was greedy / no min-tokens),
so they shouldn't ship in production runs — but they let us tell
whether the model has *learned* subtask prediction (just under EOS)
or hasn't yet. If forcing min_new_tokens=3 with temperature=0.5
produces a sensible subtask, the model is fine and just needs more
training steps to walk EOS down. If it produces gibberish, training
hasn't progressed.

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

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

@@ -365,24 +365,22 @@ class HighLevelSubtaskFwd(InferenceStep):
             return None
         ctx = _msgs_for_subtask(state)
         observation = _maybe_observation(self.observation_provider)
-        # Match training: greedy argmax, no min_new_tokens, no
-        # special-token suppression. Earlier experiments forced
-        # min_new_tokens=5 + sampling because the LM head was
-        # collapsing to EOS at position 0 — but that turned out to
-        # be a visual-distribution shift (camera frames being fed
-        # at the camera's native resolution rather than the
-        # dataset's recorded resolution), not a head pathology.
-        # With the camera frame resized to the dataset's
-        # ``ds_features['observation.images.*']['shape']`` shape,
-        # the visual prefix is back on-distribution and the same
-        # greedy decoding that works in ``--no_robot`` dry-run also
-        # works on the live robot.
+        # Default: greedy argmax, no min_new_tokens, no special-token
+        # suppression — matches training. Operator can override via
+        # ``--text_min_new_tokens=N --text_temperature=T --text_top_p=P``
+        # on the CLI; useful for under-trained checkpoints whose LM
+        # head still favours EOS at position 0 (pre-trained chat
+        # backbone's short-turn prior hasn't been fully overridden
+        # by the fine-tuning supervision yet).
         msg = _generate_with_policy(
             self.policy,
             ctx,
             observation=observation,
             state=state,
             label="subtask gen",
+            min_new_tokens=int(state.get("text_gen_min_new_tokens") or 0),
+            temperature=float(state.get("text_gen_temperature") or 0.0),
+            top_p=float(state.get("text_gen_top_p") or 1.0),
         )
         # Diagnostics: surface what the model is *actually* producing
         # at chunk boundaries, even when the output gets rejected or

src/lerobot/scripts/lerobot_smolvla2_runtime.py

@@ -259,6 +259,35 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace:
         default=None,
         help="Stop after N ticks (debug / smoke-test).",
     )
+    p.add_argument(
+        "--text_min_new_tokens",
+        type=int,
+        default=0,
+        help=(
+            "Debug knob for under-trained checkpoints: force the LM head "
+            "to emit at least N non-EOS tokens before EOS is allowed. "
+            "Use when the head's prior at position 0 still favours EOS "
+            "(short training run on a chat-pretrained backbone). 3-5 "
+            "is usually enough to reveal whether the model has real "
+            "subtask-token mass under the EOS argmax."
+        ),
+    )
+    p.add_argument(
+        "--text_temperature",
+        type=float,
+        default=0.0,
+        help=(
+            "Sampling temperature for high-level text gen. 0 = greedy "
+            "argmax (default, matches training). Set 0.3-0.7 with an "
+            "under-trained checkpoint to escape stuck-at-EOS argmax."
+        ),
+    )
+    p.add_argument(
+        "--text_top_p",
+        type=float,
+        default=1.0,
+        help="Nucleus filtering for high-level text gen.",
+    )
     p.add_argument("-v", "--verbose", action="store_true", help="Enable DEBUG logging.")
     return p.parse_args(argv)
 
@@ -1296,6 +1325,14 @@ def main(argv: list[str] | None = None) -> int:
         ctrl_hz=args.ctrl_hz,
         high_level_hz=args.high_level_hz,
     )
+    # Stash text-gen knobs on the state dict so the high-level steps
+    # (which read state) can pick them up and forward them to
+    # policy.select_message. Letting the operator try
+    # ``--text_min_new_tokens=5 --text_temperature=0.6`` on an
+    # under-trained checkpoint without recompiling.
+    runtime.state["text_gen_min_new_tokens"] = int(getattr(args, "text_min_new_tokens", 0) or 0)
+    runtime.state["text_gen_temperature"] = float(getattr(args, "text_temperature", 0.0) or 0.0)
+    runtime.state["text_gen_top_p"] = float(getattr(args, "text_top_p", 1.0) or 1.0)
     if args.task:
         runtime.set_task(args.task)
     # Seed plan/memory/subtask so the first prompt the runtime builds