feat(annotate): improve VLM subtask annotation (legible contact sheets, seeded relabeling, self-hosted vLLM recipe) (#3896)

* feat(annotate): WGO-tuned subtask prompt (atomic completed-events + duration prior)

Rework the plan-module subtask segmentation prompt toward the WGO-Bench
atomic annotation protocol: segment by completed world-state changes
(grasp/place/open/close/pour/insert), fold approach+retreat into their
event, keep separate events separate, and add a 2-10s duration prior.
Drops the pi0.7 "fewer larger composites preferred" bias that drove
under-segmentation on the benchmark. Output JSON shape unchanged.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): seeded-relabeling second pass for subtasks

Add an opt-in relabel pass (plan.subtask_seeded_relabel) that, after
segmentation, re-labels each span using previous/current/next segment
contact sheets and the seed label as a strong prior, minimally correcting
it. Mirrors macrodata's best end-to-end labeling step. Boundaries are
untouched; one extra VLM call per span. Off by default.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): robust OpenAI-compat client for hosted VLMs

Guard against a choice with no message (safety filter or a thinking model
that spends its whole budget before emitting content) so one empty reply
no longer crashes the whole annotation run; treat it as an empty response
and let the existing JSON-retry path handle it.

Add an optional `reasoning_effort` knob on VlmConfig, forwarded to the
server when set, to cap a thinking model's reasoning (needed for Gemini
via its OpenAI-compatible endpoint).

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): legible tile-scaled timestamp on contact sheets

The burned-in timestamp used the ~10px bitmap default font, which blurs
once the model downsamples a full contact sheet into 768px tiles, so the
VLM can no longer read the exact source time a boundary depends on. Scale
the timestamp to the tile height (with a graceful fallback on older
Pillow) so the visual time cue stays readable at sheet resolution.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): lean GEPA-aligned subtask segmentation prompt

Replace the verbose, label-heavy segmentation prompt with a lean
adaptation of the blog's GEPA-found completed_events_duration_prior
recipe: focus on completed manipulation events, explicit no-split /
no-merge rules, a 2-10s duration prior, and an instruction to prioritize
temporally correct boundaries over label wording. The previous prompt
over-weighted label guidance, which traded away boundary precision.

Co-authored-by: Cursor <cursoragent@cursor.com>

* revert: restore original subtask segmentation prompt

The lean GEPA-aligned paraphrase (dd4b0110d) regressed Gemini on the
30-ep subset: Seg F1 0.259 -> 0.189 and E2E 0.184 -> 0.135, driven by
worse under-segmentation (224 -> 188 preds). The blog's 0.306 came from
the actual GEPA-search artifact, which a hand paraphrase does not
reproduce. Restore the original prompt, which remains our best config.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): env-var override for prompt templates

Allow LEROBOT_PROMPT_OVERRIDE_<name> to supersede the packaged prompt
file at load time. Enables prompt search (GEPA) to inject candidate
segmentation prompts into a remote annotate job via an env secret,
without committing a branch per candidate.

Co-authored-by: Cursor <cursoragent@cursor.com>

* docs(annotate): genericize hosted-VLM comments (no model name)

Co-authored-by: Cursor <cursoragent@cursor.com>

* docs(annotate): document seeded-relabel and reasoning_effort flags

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(annotate): update subtask-prompt marker to match WGO-tuned prompt

The three plan-module tests keyed the canned VLM responder on the
literal 'atomic subtasks', which the WGO-tuned segmentation prompt no
longer contains (it now segments 'COMPLETED manipulation events'). Point
the fixture markers at the current wording so the subtask call is matched
again.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>

docs/source/annotation_pipeline.mdx

@@ -81,6 +81,12 @@ merged. Both prompts also carry a causal **event-boundary** definition (a
 new event starts when an object becomes held / is released / reaches a new
 location / a lid changes state / contents move) to sharpen where cuts land.
 
+Optionally, a third **seeded-relabel** pass (`--plan.subtask_seeded_relabel`)
+revisits each span with its previous/current/next segment contact sheets and
+minimally corrects the label, using the first label as a prior — it keeps the
+boundaries fixed and only sharpens wording, at the cost of one extra call per
+subtask.
+
 The resulting spans are then stitched into a gap-free, full-episode
 cover, so **every frame has exactly one active subtask**. See
 [`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
@@ -157,30 +163,33 @@ Every module is on by default and can be toggled independently (set to
 
 ### The VLM (`--vlm.*`)
 
-| Flag                       | Default            | What it does                                                                        |
-| -------------------------- | ------------------ | ----------------------------------------------------------------------------------- |
-| `--vlm.model_id`           | `Qwen/Qwen3.6-27B` | The model to serve and prompt.                                                      |
-| `--vlm.camera_key`         | first `images.*`   | Which camera every prompt is grounded on.                                           |
-| `--vlm.serve_command`      | auto               | The exact `vllm serve …` command (set TP size, GPU memory, `--max-model-len` here). |
-| `--vlm.parallel_servers`   | `1`                | Independent servers for round-robin routing (one per GPU).                          |
-| `--vlm.num_gpus`           | `0`                | GPUs per server (`0` = one each).                                                   |
-| `--vlm.client_concurrency` | `16`               | In-flight requests across all servers.                                              |
-| `--vlm.max_new_tokens`     | `512`              | Generation cap per call.                                                            |
-| `--vlm.temperature`        | `0.2`              | Sampling temperature.                                                               |
+| Flag                       | Default            | What it does                                                                         |
+| -------------------------- | ------------------ | ------------------------------------------------------------------------------------ |
+| `--vlm.model_id`           | `Qwen/Qwen3.6-27B` | The model to serve and prompt.                                                       |
+| `--vlm.camera_key`         | first `images.*`   | Which camera every prompt is grounded on.                                            |
+| `--vlm.serve_command`      | auto               | The exact `vllm serve …` command (set TP size, GPU memory, `--max-model-len` here).  |
+| `--vlm.parallel_servers`   | `1`                | Independent servers for round-robin routing (one per GPU).                           |
+| `--vlm.num_gpus`           | `0`                | GPUs per server (`0` = one each).                                                    |
+| `--vlm.client_concurrency` | `16`               | In-flight requests across all servers.                                               |
+| `--vlm.max_new_tokens`     | `512`              | Generation cap per call.                                                             |
+| `--vlm.temperature`        | `0.2`              | Sampling temperature.                                                                |
+| `--vlm.reasoning_effort`   | `null`             | Thinking-budget hint (`low`/`medium`/`high`) forwarded to OpenAI-compatible servers. |
 
 ### Subtasks / plan / memory (`--plan.*`)
 
-| Flag                            | Default    | What it does                                                                                                              |
-| ------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------- |
-| `--plan.frames_per_second`      | `2.0`      | Frame sampling rate for the contact sheets (`2.0` = one frame every 0.5s).                                                |
-| `--plan.max_frames_per_prompt`  | `60`       | Frame budget per VLM call. Episodes whose sampling exceeds this are auto-windowed at the same density, then stitched.     |
-| `--plan.contact_sheet_columns`  | `5`        | Columns per contact-sheet grid (`contact_sheet_frames_per_sheet` tiles, time row-major).                                  |
-| `--plan.plan_max_steps`         | `8`        | Upper bound on subtasks per episode.                                                                                      |
-| `--plan.subtask_describe_first` | `true`     | Run the describe→segment grounding pass (best subtask quality; +1 call/episode).                                          |
-| `--plan.emit_plan`              | `true`     | Emit the numbered `plan` rows (`false` = subtasks + memory only).                                                         |
-| `--plan.emit_memory`            | `true`     | Emit the `memory` rows (`false` = subtasks + plan only); symmetric to `emit_plan`.                                        |
-| `--plan.n_task_rephrasings`     | `10`       | How many `task_aug` rephrasings to emit (`0` disables).                                                                   |
-| `--plan.derive_task_from_video` | `if_short` | Use the dataset task as-is (`off`), only when it's missing/short (`if_short`), or always re-derive from video (`always`). |
+| Flag                            | Default    | What it does                                                                                                                 |
+| ------------------------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------- |
+| `--plan.frames_per_second`      | `2.0`      | Frame sampling rate for the contact sheets (`2.0` = one frame every 0.5s).                                                   |
+| `--plan.max_frames_per_prompt`  | `60`       | Frame budget per VLM call. Episodes whose sampling exceeds this are auto-windowed at the same density, then stitched.        |
+| `--plan.contact_sheet_columns`  | `5`        | Columns per contact-sheet grid (`contact_sheet_frames_per_sheet` tiles, time row-major).                                     |
+| `--plan.plan_max_steps`         | `8`        | Upper bound on subtasks per episode.                                                                                         |
+| `--plan.subtask_describe_first` | `true`     | Run the describe→segment grounding pass (best subtask quality; +1 call/episode).                                             |
+| `--plan.subtask_seeded_relabel` | `false`    | Second pass: re-label each subtask from its prev/current/next contact sheets, seeded with the first label (+1 call/subtask). |
+| `--plan.subtask_relabel_frames` | `5`        | Frames sampled uniformly per segment sheet in the relabel pass (only used when `subtask_seeded_relabel=true`).               |
+| `--plan.emit_plan`              | `true`     | Emit the numbered `plan` rows (`false` = subtasks + memory only).                                                            |
+| `--plan.emit_memory`            | `true`     | Emit the `memory` rows (`false` = subtasks + plan only); symmetric to `emit_plan`.                                           |
+| `--plan.n_task_rephrasings`     | `10`       | How many `task_aug` rephrasings to emit (`0` disables).                                                                      |
+| `--plan.derive_task_from_video` | `if_short` | Use the dataset task as-is (`off`), only when it's missing/short (`if_short`), or always re-derive from video (`always`).    |
 
 ### Interjections + VQA
 

src/lerobot/annotations/steerable_pipeline/config.py

@@ -65,6 +65,14 @@ class PlanConfig:
     # invented from the task text (+1 VLM call/episode).
     subtask_describe_first: bool = True
 
+    # Seeded relabeling: after segmentation, re-label each span with a focused
+    # pass that sees the previous / current / next segment contact sheets and
+    # minimally corrects the seed label (macrodata's best end-to-end labeling
+    # step). Costs +1 VLM call per subtask; off by default.
+    subtask_seeded_relabel: bool = False
+    # Frames sampled uniformly per segment sheet in the relabel pass.
+    subtask_relabel_frames: int = 5
+
     # Emit ``style="plan"`` rows at each boundary; False = subtasks + memory only.
     emit_plan: bool = True
 
@@ -160,6 +168,11 @@ class VlmConfig:
     # Forwarded as extra_body.chat_template_kwargs (e.g. {"enable_thinking": false}).
     chat_template_kwargs: dict[str, Any] | None = None
 
+    # OpenAI-style thinking budget hint ("low"/"medium"/"high"); forwarded to
+    # the server when set. Used to cap a thinking model's reasoning so it
+    # leaves tokens for the actual JSON answer on OpenAI-compatible endpoints.
+    reasoning_effort: str | None = None
+
 
 @dataclass
 class ExecutorConfig:

src/lerobot/annotations/steerable_pipeline/frames.py

@@ -413,7 +413,16 @@ def _draw_timestamp_badge(image: PIL.Image.Image, timestamp: float) -> PIL.Image
 
     result = image.copy()
     draw = ImageDraw.Draw(result)
-    font = ImageFont.load_default()
+    # Scale the timestamp to the tile so it stays legible after the model
+    # downsamples the full sheet into 768px tiles — a tiny bitmap font blurs
+    # at contact-sheet resolution and the VLM can no longer read the exact
+    # source time, which is what the boundary score depends on. ``size=`` is
+    # supported by Pillow's bitmap default since 10.1; fall back otherwise.
+    badge_px = max(14, round(image.height * 0.12))
+    try:
+        font = ImageFont.load_default(size=badge_px)
+    except TypeError:
+        font = ImageFont.load_default()
     label = f"{timestamp:06.2f}s"
     left, top, right, bottom = draw.textbbox((0, 0), label, font=font)
     text_w, text_h = right - left, bottom - top

src/lerobot/annotations/steerable_pipeline/modules/plan_subtasks_memory.py

@@ -116,6 +116,8 @@ class PlanSubtasksMemoryModule:
             rows.extend(self._task_aug_rows([effective_task, *variants], t0))
 
         subtask_spans = self._generate_subtasks(record, task=effective_task)
+        if self.config.subtask_seeded_relabel and subtask_spans:
+            subtask_spans = self._seeded_relabel(record, subtask_spans, effective_task)
 
         # subtask rows
         for span in subtask_spans:
@@ -509,6 +511,51 @@ class PlanSubtasksMemoryModule:
 
         return cleaned
 
+    def _seeded_relabel(
+        self, record: EpisodeRecord, spans: list[dict[str, Any]], task: str
+    ) -> list[dict[str, Any]]:
+        """Re-label each span using prev/current/next segment contact sheets.
+
+        Boundaries are kept fixed; only ``text`` is refined. The original
+        ("seed") label is passed as a strong prior so the model verifies and
+        minimally corrects it rather than re-describing from scratch — the
+        macrodata seeded-relabeling step. One VLM call per span.
+        """
+        n = len(spans)
+        out: list[dict[str, Any]] = []
+        for i, span in enumerate(spans):
+            content: list[dict[str, Any]] = []
+            if i > 0:
+                content += self._segment_sheet(record, spans[i - 1])
+            content += self._segment_sheet(record, span)
+            if i < n - 1:
+                content += self._segment_sheet(record, spans[i + 1])
+            prompt = load_prompt("plan_subtask_relabel").format(
+                episode_task=task,
+                seed_label=span["text"],
+                segment_index=i + 1,
+                segment_count=n,
+                start=float(span["start"]),
+                end=float(span["end"]),
+            )
+            content.append({"type": "text", "text": prompt})
+            label = self._vlm_field([{"role": "user", "content": content}], "label")
+            text = label.strip() if isinstance(label, str) and label.strip() else span["text"]
+            out.append({**span, "text": text})
+        return out
+
+    def _segment_sheet(self, record: EpisodeRecord, span: dict[str, Any]) -> list[dict[str, Any]]:
+        """Contact-sheet block(s) for one span: up to N frames sampled uniformly."""
+        s, e = float(span["start"]), float(span["end"])
+        n = max(1, int(self.config.subtask_relabel_frames))
+        if e <= s or n == 1:
+            timestamps = [s]
+        else:
+            step = (e - s) / (n - 1)
+            timestamps = [s + i * step for i in range(n)]
+        frames = self.frame_provider.frames_at(record, timestamps)
+        return self._contact_sheet_blocks(frames, timestamps[: len(frames)])
+
     def _generate_subtasks_windowed(
         self, record: EpisodeRecord, task: str, window_s: float
     ) -> list[dict[str, Any]]:

src/lerobot/annotations/steerable_pipeline/prompts/__init__.py

@@ -22,12 +22,23 @@ plain editors and roundtrip cleanly through ``ruff format``.
 
 from __future__ import annotations
 
+import os
 from pathlib import Path
 
 _DIR = Path(__file__).parent
 
 
 def load(name: str) -> str:
-    """Read prompt template ``name.txt`` from the ``prompts/`` directory."""
+    """Read prompt template ``name.txt`` from the ``prompts/`` directory.
+
+    A ``LEROBOT_PROMPT_OVERRIDE_<name>`` environment variable, when set to a
+    non-empty value, takes precedence over the packaged file. This lets prompt
+    search (e.g. GEPA) inject candidate templates into a remote job without
+    rebuilding the package; the override must keep the same ``{placeholder}``
+    fields the call site formats in.
+    """
+    override = os.environ.get(f"LEROBOT_PROMPT_OVERRIDE_{name}")
+    if override and override.strip():
+        return override
     path = _DIR / f"{name}.txt"
     return path.read_text(encoding="utf-8")

src/lerobot/annotations/steerable_pipeline/prompts/plan_subtask_relabel.txt

@@ -0,0 +1,35 @@
+Annotate one fixed segment from a longer robot demonstration.
+
+Return only JSON:
+  {{"label": "<short descriptive subtask label>"}}
+
+You are shown up to three timestamped contact sheets, in order:
+- The FIRST sheet is the PREVIOUS segment (context only); it may be absent.
+- The SECOND sheet is the CURRENT target segment.
+- The THIRD sheet is the NEXT segment (context only); it may be absent.
+Each tile has its timestamp (seconds, absolute video time) burned into its
+top-left corner.
+
+Episode instruction: "{episode_task}"
+Target segment: {segment_index} of {segment_count}
+Target time: {start:.2f}s to {end:.2f}s
+Original predicted label for this exact segment: "{seed_label}"
+
+Rules:
+- Label ONLY the current target segment (the second sheet). Use the
+  previous/next sheets only to disambiguate what changed.
+- Treat the original predicted label as a STRONG PRIOR, not ground truth:
+  verify it against the current segment and correct it minimally.
+- If it already names the right action and main object, keep it; only fix
+  grammar or add a clearly visible essential detail.
+- If it is vague but directionally correct, make it more specific.
+- If it describes the previous/next segment, the wrong action, wrong
+  object, wrong destination, or a wrong state change, replace it.
+- Do not describe the previous or next segment, and do not split, merge,
+  or move the fixed segment.
+- Do not introduce an action that is not clearly visible in the current
+  target segment.
+- Use one concise imperative phrase. Name the manipulated object and the
+  action / state change. Include source, destination, side, direction,
+  final placement, or opened/closed state when visible and central.
+- Do not mention timestamps, frame numbers, uncertainty, or intent.

src/lerobot/annotations/steerable_pipeline/prompts/plan_subtasks.txt

@@ -1,112 +1,68 @@
-You are labeling a teleoperated robot demonstration.
+You are annotating a teleoperated robot demonstration shown as
+timestamped contact sheets (each tile has its time in seconds burned
+into the top-left corner). The operator's goal was: "{episode_task}"
 
-The user originally asked: "{episode_task}"
+{observation_block}Reconstruct the sequence of COMPLETED manipulation events the robot
+performs, in chronological order. Output one segment per event with a
+[start, end] time in seconds and a short action label.
 
-You are shown the entire demonstration as a single video. Watch the
-whole clip, then segment it into a list of consecutive atomic subtasks
-the robot performs.
+GROUNDING — read first, it overrides everything below:
+- Label ONLY events you can SEE in the frames. The instruction is the
+  goal; the VIDEO is the ground truth for what actually happened.
+- Do NOT invent, anticipate, or pad steps that are not shown.
 
-{observation_block}GROUNDING — read this first, it overrides everything below:
-- Label ONLY what the robot actually does in the video. Every subtask
-  you emit must correspond to motion you can SEE in specific frames.
-- Do NOT invent, anticipate, or pad. If the robot only does one thing
-  (e.g. it just navigates to a location and the clip ends), emit
-  EXACTLY ONE subtask. Many demonstrations are a single atomic skill.
-- ``max_steps`` below is a hard CEILING, not a target. Emitting fewer
-  subtasks than the ceiling is not just allowed, it is expected for
-  short / atomic demonstrations. One correct subtask is far better
-  than several invented ones.
-- If the video does not clearly show the action implied by the task,
-  describe what you actually see — do NOT fabricate the task's steps
-  from the instruction text. The instruction tells you the goal; the
-  VIDEO is the ground truth for what happened.
+Granularity — segment by completed events, not by motion:
+- Start a NEW segment whenever the world state changes: an object is
+  grasped, lifted, transported, placed, or released; a held object
+  changes; a drawer/door/lid/container opens or closes; contents move
+  between containers (poured); a tool starts or stops acting on a
+  surface. Watch the gripper open/close transitions — they usually mark
+  boundaries.
+- Do NOT split approach, reach, grasp adjustment, small repositioning,
+  hesitation, or retreat into their own segments. Fold each into the
+  event it belongs to (the approach is part of the pick; the retreat is
+  part of the place).
+- Do NOT merge separate completed events. Each distinct pick, place,
+  open, close, pour, push, wipe, or insert is its own segment, even when
+  they repeat on different objects or locations.
+- Most segments last 2-10 seconds. Shorter segments are okay ONLY for
+  fast pick / place / open / close / release events. Never emit a
+  segment shorter than {min_subtask_seconds} seconds; merge a too-short
+  candidate into its neighbour instead.
+- Skip idle time, pure camera motion, and tiny hand jitter.
 
-Authoring rules — Hi Robot atom granularity, pi0.7-style short prompts:
+Labels — short imperative phrases:
+- One concise command naming the action and the manipulated object, e.g.
+  "pick up the red cup", "put the cup on the shelf", "open the top
+  drawer", "pour water into the glass", "insert the plug into the
+  socket".
+- Include source, destination, side, direction, or the final
+  open/closed state when it is visible and central to the event.
+- Prefer these verbs (extend only when none fits): pick up, put, place,
+  push, pull, turn, press, open, close, pour, insert, wipe, stack.
+  Disambiguate by what you SEE:
+    * STACK vs PUT: object placed ON TOP OF another object -> "stack".
+    * INSERT vs PUT: object pushed INTO a fitted slot/hole/socket -> "insert".
+    * PICK UP vs PUT (direction): gripper CLOSES and object moves WITH
+      the hand -> "pick up"; gripper OPENS and object stays -> "put".
+    * POUR vs PUT: source is tilted and contents flow -> "pour".
+- Use the exact object nouns implied by the task; stay consistent across
+  the episode (don't switch "cube" to "block").
+- Write imperative commands, never third person ("the robot ..."), and
+  drop articles/adverbs.
 
-- Each subtask = one COMPOSITE atomic skill the low-level policy can
-  execute end-to-end. A "skill" bundles its own approach motion with
-  its terminal action — do NOT split the approach off as its own
-  subtask. The whole-arm policy already learns to reach as part of
-  every manipulation primitive.
-- Write each subtask as an IMPERATIVE COMMAND, starting with one of
-  these verbs (extend only when none fits):
-    pick up <obj>           — approach + grasp + lift in one subtask
-    put <obj> on/in <loc>   — transport + release in one subtask
-    place <obj> on/in <loc> — synonym of "put"; pick one and stay consistent
-    push <obj>              — contact + linear shove
-    pull <obj>              — contact + linear retract
-    turn <knob/dial/handle> — rotary actuation
-    press <button>          — single-press contact
-    open <drawer/door/lid>  — full open motion
-    close <drawer/door/lid> — full close motion
-    pour <src> into <dst>   — tilt + flow
-    insert <obj> into <slot>— alignment + push-fit
-    go to <loc>             — ONLY when no grasp / actuation follows
-                             (e.g. a pure relocation between phases).
-                             If the next subtask grasps something at
-                             that location, drop "go to ..." and just
-                             write "pick up ..." instead.
-- Forbidden ultra-fine splits — the VLM is NOT allowed to emit these
-  as standalone subtasks; fold them into the parent composite:
-    "move to X"   → fold into "pick up X" (or whatever follows)
-    "reach for X" → fold into "pick up X"
-    "grasp X"     → fold into "pick up X"
-    "lift X"      → fold into "pick up X" (or "put X on Y" if it's
-                    the transport phase of a place)
-    "release X"   → fold into "put X on Y" (or "place X in Y")
-- Keep it SHORT — a verb phrase, not a sentence. Drop articles
-  ("the", "a") and adverbs ("carefully", "slowly"). Add a "how"
-  detail (which hand, which grasp point) ONLY when it is needed to
-  disambiguate. Every subtask must begin with one of the verbs
-  above (no leading nouns, no "then", no "first").
-- NEVER use third person. Never write "the robot", "the arm", "the
-  gripper moves", "it picks up" — the robot is implied. Command it,
-  do not describe it.
-- Use the exact object nouns from the task above. If the task says
-  "cube", every subtask says "cube" — never switch to "block". If it
-  says "box", never switch to "bin"/"container". Keep vocabulary
-  consistent across the whole episode.
-- Good: "pick up blue cube", "put blue cube in box", "open drawer",
-  "turn red knob", "press start button", "go to sink".
-- Bad: "move to blue cube" (approach as its own subtask — forbidden,
-  must be folded into "pick up blue cube"); "the robot arm moves
-  towards the blue cube" (third person, too long); "carefully pick
-  up the cube" (adverb, article); "release the yellow block"
-  ("block" when the task said "cube", and "release" must be folded
-  into a "put"/"place" subtask).
-- Subtasks are non-overlapping and cover the full episode in order.
-  Choose the cut points yourself based on what you see in the video
-  (gripper open/close events, contact, regrasps, transitions).
-- Each subtask spans at least {min_subtask_seconds} seconds. If a
-  candidate span would be shorter, merge it into its neighbour
-  rather than emitting it.
-- Do not exceed {max_steps} subtasks total. Fewer, larger composites
-  are preferred over many micro-steps.
-- Every subtask's [start_time, end_time] must lie within
-  [0.0, {episode_duration}] seconds.
-
-SPECIAL CASES — verb disambiguation (each rule is narrowly visual and
-fires ONLY on the spatial situation it names; it must not change how you
-label any other situation):
-- STACK vs PUT: if an object is placed ON TOP OF another specific object
-  (not on a flat table / shelf / counter), use "stack ... on ...", not
-  "put". "stack blue book on green book", NOT "put blue book on table".
-- INSERT vs PUT: if an object goes INTO a fitted slot / hole / socket /
-  receptacle (push-fit), use "insert ... into ...", not "put".
-- RETRIEVE/PICK-UP vs PUT (direction): watch the gripper. If it CLOSES
-  on the object and the object moves WITH the hand, it is "pick up" /
-  "retrieve" (object leaves its location). If the gripper OPENS and the
-  object stays where the hand left it, it is "put" / "place" (object
-  arrives at a location). Decide by which way the object moves, not by
-  where the hand ends up.
-- POUR vs PUT: only use "pour" when the source is tilted and contents
-  flow out; moving a full container without tilting is "put"/"place".
+Timing:
+- Use the burned-in timestamps to set start and end. Boundaries should
+  land on or near a printed time, and every [start, end] must lie within
+  [0.0, {episode_duration}] seconds, be non-overlapping, and cover the
+  episode in order.
+- Emit at most {max_steps} segments.
 
 Output strictly valid JSON of shape:
 
   {{
     "subtasks": [
-      {{"text": "<short imperative verb phrase>", "start": <float>, "end": <float>}},
+      {{"text": "<short imperative action label>", "start": <float>, "end": <float>}},
       ...
     ]
   }}

src/lerobot/annotations/steerable_pipeline/vlm_client.py

@@ -285,6 +285,8 @@ def _make_openai_client(config: VlmConfig) -> VlmClient:
             "max_tokens": max_tok,
             "temperature": temp,
         }
+        if config.reasoning_effort:
+            kwargs["reasoning_effort"] = config.reasoning_effort
         extra_body: dict[str, Any] = {}
         if send_mm_kwargs and mm_kwargs:
             extra_body["mm_processor_kwargs"] = {**mm_kwargs, "do_sample_frames": True}
@@ -296,7 +298,13 @@ def _make_openai_client(config: VlmConfig) -> VlmClient:
             chosen = clients[rr_counter["i"] % len(clients)]
             rr_counter["i"] += 1
         response = chosen.chat.completions.create(**kwargs)
-        return response.choices[0].message.content or ""
+        # Some OpenAI-compatible servers can return a choice with no message
+        # (safety filter, or a "thinking" model that spends the whole budget
+        # before emitting content). Treat that as an empty reply so the
+        # JSON-retry path handles it instead of crashing the run.
+        choice = response.choices[0] if response.choices else None
+        message = choice.message if choice is not None else None
+        return (message.content if message is not None else None) or ""
 
     def _gen(batch: Sequence[Sequence[dict[str, Any]]], max_tok: int, temp: float) -> list[str]:
         if len(batch) <= 1 or config.client_concurrency <= 1:

tests/annotations/test_modules.py

@@ -85,7 +85,7 @@ def _spy_responder(captured: list[list[dict[str, Any]]], reply: Any):
 def test_module1_plan_memory_subtask_smoke(fixture_dataset_root: Path, tmp_path: Path) -> None:
     vlm = make_canned_responder(
         {
-            "atomic subtasks": {
+            "COMPLETED manipulation events": {
                 "subtasks": [
                     {"text": "grasp the handle of the sponge", "start": 0.0, "end": 0.4},
                     {"text": "wipe the counter from left to right", "start": 0.4, "end": 0.8},
@@ -126,7 +126,7 @@ def test_module1_emit_memory_false_skips_memory_keeps_subtasks_and_plan(
     leaving subtask + plan generation intact — symmetric to ``emit_plan``."""
     vlm = make_canned_responder(
         {
-            "atomic subtasks": {
+            "COMPLETED manipulation events": {
                 "subtasks": [
                     {"text": "grasp the handle of the sponge", "start": 0.0, "end": 0.4},
                     {"text": "wipe the counter from left to right", "start": 0.4, "end": 0.8},
@@ -318,7 +318,7 @@ def test_module1_attaches_contact_sheets_to_subtask_prompt(
                     return block.get("text", "")
         return ""
 
-    subtask_calls = [m for m in captured if "atomic subtasks" in _prompt_text(m)]
+    subtask_calls = [m for m in captured if "COMPLETED manipulation events" in _prompt_text(m)]
     assert len(subtask_calls) == 1, "expected exactly one subtask-prompt VLM call"
     content = subtask_calls[0][0]["content"]
     video_blocks = [b for b in content if isinstance(b, dict) and b.get("type") == "video"]