fix(annotation): remove dropout when doing annotation

src/lerobot/annotations/steerable_pipeline/config.py

@@ -176,7 +176,7 @@ class AdvantageConfig:
     enabled: bool = True
 
     # Constant advantage label for all frames (e.g. "positive" for SFT iteration 0).
-    # Skips VF inference, dropout still applies for CFG.
+    # Skips VF inference.
     constant_value: str | None = None
 
     # Trained value function checkpoint (local path or Hub repo ID).
@@ -195,9 +195,6 @@ class AdvantageConfig:
     # Actions with advantage > ε_ℓ get I_t = True (positive).
     threshold_percentile: float = 0.3
 
-    # Fraction of frames to randomly omit advantage labels (enables CFG).
-    dropout_rate: float = 0.3
-
     # Force I_t = True for frames marked as human interventions.
     force_positive_on_intervention: bool = True
 
@@ -210,9 +207,6 @@ class AdvantageConfig:
     # Batch size for value function inference.
     batch_size: int = 32
 
-    # Random seed for dropout reproducibility.
-    seed: int = 1729
-
 
 @dataclass
 class AnnotationPipelineConfig:

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

@@ -262,13 +262,8 @@ class AdvantageModule:
 
         threshold = self._compute_threshold(advantages, intervention_mask)
 
-        rng = np.random.default_rng(seed=self.config.seed + record.episode_index)
-
         rows: list[dict[str, Any]] = []
         for t in range(num_frames):
-            if rng.random() < self.config.dropout_rate:
-                continue
-
             if (
                 self.config.force_positive_on_intervention
                 and intervention_mask[t]
@@ -303,15 +298,11 @@ class AdvantageModule:
         )
 
     def _run_constant_mode(self, record: EpisodeRecord, staging: EpisodeStaging) -> None:
-        """Emit a fixed advantage value for every frame (with dropout for CFG)."""
+        """Emit a fixed advantage value for every frame."""
         num_frames = len(record.frame_timestamps)
-        rng = np.random.default_rng(seed=self.config.seed + record.episode_index)
 
         rows: list[dict[str, Any]] = []
         for t in range(num_frames):
-            if rng.random() < self.config.dropout_rate:
-                continue
-
             rows.append(
                 {
                     "role": "user",
@@ -325,12 +316,11 @@ class AdvantageModule:
 
         staging.write("advantage", rows)
         logger.debug(
-            "Episode %d: %d/%d frames labeled constant '%s' (dropout=%.2f)",
+            "Episode %d: %d/%d frames labeled constant '%s'",
             record.episode_index,
             len(rows),
             num_frames,
             self.config.constant_value,
-            self.config.dropout_rate,
         )
 
     def _compute_threshold(self, advantages: np.ndarray, intervention_mask: np.ndarray) -> float:

tests/annotations/test_advantage.py

@@ -111,7 +111,6 @@ def test_binarization_with_mock_values(staging: EpisodeStaging):
 
     cfg = AdvantageConfig(
         value_function_path="/fake/vf",
-        dropout_rate=0.0,
         threshold_percentile=0.5,
     )
     module = AdvantageModule(config=cfg)
@@ -146,7 +145,6 @@ def test_intervention_frames_forced_positive(staging: EpisodeStaging):
 
     cfg = AdvantageConfig(
         value_function_path="/fake/vf",
-        dropout_rate=0.0,
         force_positive_on_intervention=True,
     )
     module = AdvantageModule(config=cfg)
@@ -163,16 +161,13 @@ def test_intervention_frames_forced_positive(staging: EpisodeStaging):
     assert rows[2]["content"] == "positive"
 
 
-def test_dropout_reduces_output_rows(staging: EpisodeStaging):
+def test_all_frames_labeled(staging: EpisodeStaging):
-    """Non-zero dropout rate omits some frames."""
+    """Every frame gets an advantage label (no annotation-level dropout)."""
     num_frames = 100
     mc_returns = np.linspace(-0.9, -0.1, num_frames).astype(np.float32)
     mock_values = np.full(num_frames, -0.5, dtype=np.float32)
 
-    cfg = AdvantageConfig(
+    cfg = AdvantageConfig(value_function_path="/fake/vf")
-        value_function_path="/fake/vf",
-        dropout_rate=0.3,
-    )
     module = AdvantageModule(config=cfg)
     record = _make_record(num_frames=num_frames, mc_returns=mc_returns)
 
@@ -183,8 +178,7 @@ def test_dropout_reduces_output_rows(staging: EpisodeStaging):
         module.run_episode(record, staging)
 
     rows = staging.read("advantage")
-    # With 30% dropout on 100 frames, expect ~70 rows (with some variance)
+    assert len(rows) == num_frames
-    assert 50 < len(rows) < 90
 
 
 def test_staged_row_format(staging: EpisodeStaging):
@@ -193,10 +187,7 @@ def test_staged_row_format(staging: EpisodeStaging):
     mc_returns = np.array([-0.5, -0.4, -0.3, -0.2, -0.1], dtype=np.float32)
     mock_values = np.full(5, -0.3, dtype=np.float32)
 
-    cfg = AdvantageConfig(
+    cfg = AdvantageConfig(value_function_path="/fake/vf")
-        value_function_path="/fake/vf",
-        dropout_rate=0.0,
-    )
     module = AdvantageModule(config=cfg)
     record = _make_record(num_frames=num_frames, mc_returns=mc_returns)
 
@@ -225,7 +216,6 @@ def test_n_step_advantage():
     cfg = AdvantageConfig(
         value_function_path="/fake/vf",
         n_step=3,
-        dropout_rate=0.0,
     )
     module = AdvantageModule(config=cfg)
     record = _make_record(num_frames=num_frames, mc_returns=mc_returns)