diff --git a/schema/learning_sets.json b/schema/learning_sets.json
new file mode 100644
index 00000000..342d231d
--- /dev/null
+++ b/schema/learning_sets.json
@@ -0,0 +1,24065 @@
+{
+    "pkg_location": "aind_behavior_vr_foraging_curricula.learning_sets",
+    "name": "LearningSets",
+    "version": "1.1.1-rc1",
+    "graph": {
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+                    "description": "",
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+                                    "on_failure": 0.0,
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+                                    "maximum": 2.9
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+                                            10,
+                                            2,
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+                                            2,
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+                                            7,
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+                                            4,
+                                            7,
+                                            7,
+                                            4,
+                                            4,
+                                            7,
+                                            4,
+                                            4
+                                        ],
+                                        "sampling_mode": "Ordered"
+                                    },
+                                    "end_conditions": []
+                                }
+                            ],
+                            "sampling_mode": "Sequential"
+                        },
+                        "operation_control": {
+                            "movable_spout_control": {
+                                "enabled": false,
+                                "time_to_collect_after_reward": 1.0,
+                                "retracting_distance": 0.0
+                            },
+                            "odor_control": {
+                                "target_total_flow": 1000,
+                                "target_odor_flow": 100
+                            },
+                            "position_control": {
+                                "gain": {
+                                    "x": 1.0,
+                                    "y": 1.0,
+                                    "z": 1.0
+                                },
+                                "initial_position": {
+                                    "x": 0.0,
+                                    "y": 2.56,
+                                    "z": 0.0
+                                },
+                                "frequency_filter_cutoff": 5.0,
+                                "velocity_threshold": 8.0
+                            },
+                            "audio_control": {
+                                "duration": 0.2,
+                                "frequency": 9999.0
+                            },
+                            "wait_to_start_duration": 0.0,
+                            "wait_to_finish_duration": 0.0
+                        }
+                    },
+                    "version": "1.1.1-rc1",
+                    "stage_name": "graduated"
+                },
+                "graph": {
+                    "nodes": {
+                        "0": "aind_behavior_vr_foraging_curricula.learning_sets.policies.p_introduce_negative_sites",
+                        "1": "aind_behavior_vr_foraging_curricula.learning_sets.policies.p_water_cap"
+                    },
+                    "graph": {
+                        "0": [],
+                        "1": []
+                    }
+                },
+                "start_policies": [
+                    "aind_behavior_vr_foraging_curricula.learning_sets.policies.p_introduce_negative_sites",
+                    "aind_behavior_vr_foraging_curricula.learning_sets.policies.p_water_cap"
+                ],
+                "metrics_provider": "aind_behavior_vr_foraging_curricula.learning_sets.metrics.metrics_from_dataset"
+            }
+        },
+        "graph": {
+            "0": [
+                [
+                    "aind_behavior_vr_foraging_curricula.learning_sets.curriculum.st_shaping_to_graduated",
+                    1
+                ]
+            ],
+            "1": []
+        }
+    }
+}
\ No newline at end of file
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/README.md b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/README.md
new file mode 100644
index 00000000..21b94526
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/README.md
@@ -0,0 +1,83 @@
+# LearningSets
+
+A two-stage curriculum for the **learning-set** task: every session is a single block of
+single-reward-site patches drawn from 7 odors. The odor ordering is regenerated each day
+under the same pairing rules as
+[`examples/task_learning_sets.py`](../../../../../../examples/task_learning_sets.py)
+(each trial is a `(negative, positive)` pair; no odor that appeared in the last pair may
+reappear). Each odor has a *positive* variant (always rewarded, p=1) and a *negative*
+variant (always unrewarded, p=0). The curriculum shapes difficulty by gradually
+**increasing the proportion of negative sites** rather than stepping reward probability.
+
+## Background: Harlow's learning sets
+
+The paradigm is inspired by Harry Harlow's classic *learning set* ("learning to learn")
+experiments from the late 1940s–50s (Harlow, *The Formation of Learning Sets*,
+Psychological Review, 1949). Harlow gave rhesus monkeys a long series of two-choice
+object-discrimination problems: in each problem one object was consistently rewarded
+and the other was not, but the rewarded object changed from problem to problem. Early
+problems were solved slowly and trial-by-trial, but across hundreds of problems the
+animals learned the *rule* itself — that one object is always correct — and eventually
+solved each new problem in essentially a single trial ("win-stay, lose-shift"). The key
+finding was that they were not just learning individual discriminations but acquiring a
+transferable strategy: a learning set.
+
+This curriculum recreates that structure in the VR foraging task. Each day presents
+fresh `(negative, positive)` odor pairs drawn under the same rules, so the specific
+rewarded odor keeps changing while the underlying rule stays constant — letting us ask
+whether the animal forms a learning set and discriminates new pairs faster over time.
+
+## Stages
+
+| Stage | Neg sites / pair | Stop duration | Velocity threshold | Geometry | Reward µL |
+|-------|-----------------|---------------|--------------------|----------|-----------|
+| `shaping` | 0 → 1 → 3 → 5 (ramped across sessions) | ramps 0.1 → 3 s (within session) | shaped 60 → 8 cm/s | eased compressed → full | 6 default, trimmed in `graduated` |
+| `graduated` | 5 (fixed, full 5+5 ratio) | 3 s (fixed) | 8 cm/s (fixed) | full (fixed) | trimmed toward water budget |
+
+Within the `shaping` stage the difficulty knobs evolve concurrently:
+
+- **Negative-site proportion** — session 1: all 10 sites per pair are positive (n_neg = 0,
+  every stop is rewarded). Subsequent sessions walk `0 → 1 → 3 → 5` neg sites per pair via
+  `N_NEG_RAMP`, while positive sites fill the remainder to keep the total fixed at 10. This
+  means the animal first just learns to stop (session 1), then is progressively challenged
+  to discriminate which odor is worth stopping for.
+- **Speed** — the stop-velocity threshold (cm/s below which a stop counts) starts lenient
+  (60 cm/s) and a GAIN updater drives it down to the final floor (8 cm/s) across sessions.
+- **Geometry** — inter-patch spacing (mean/max `40/70 → 60/190`) and reward-site length
+  (`25 → 40` cm) ease from compressed (easy, day-1) toward full, scaled by how many sites
+  the subject travelled in the prior session (`n_patches_seen`). Inter-site spacing is
+  fixed at 15 cm.
+- **Stop duration** — a tiny operant base (0.1 s) plus a within-session offset that ramps
+  to 3 s, so on the first days a brief dip below the velocity threshold earns reward by
+  chance.
+
+## Cross-session policies (`start_policies`)
+
+- **`p_introduce_negative_sites`** (both stages) — reads `last_n_neg_sites_per_pair` from
+  prior metrics, advances to the next step in `N_NEG_RAMP = (1, 3, 5)`, and regenerates
+  `patch_indices` with the new neg/pos split. None → 0 (first session, all positive);
+  saturates at 5. In the `graduated` stage the ramp is already at 5 and stays there.
+- **`p_seed_stop_velocity`** (`shaping`) — starts the stop-velocity threshold a little
+  above where the prior session floored (`× 1.2`, clamped to bounds). Dropped from
+  `graduated`, which runs fixed at the floor.
+- **`p_seed_stop_duration`** (`shaping`) — starts the stop-duration offset a little below
+  where the prior session ended (`× 0.85`), so the subject re-ramps rather than being
+  dropped at the longest stop.
+- **`p_ease_geometry`** (`shaping`) — eases the geometry from compressed toward full,
+  scaled by sites travelled in the prior session.
+- **`p_water_cap`** (`graduated`) — if the prior session delivered more than 1.0 mL the
+  per-reward amount is trimmed by 0.5 µL; if it fell below 0.7 mL the amount is raised by
+  0.5 µL (both clamped to `[4, 8]` µL), steering total water toward the target window.
+
+## Graduation
+
+`shaping → graduated`: negative sites per pair have reached the full ratio (5) **and**
+the stop-velocity threshold has been shaped to its final floor (8 cm/s) **and** the
+subject is discriminating (visit ratio ≤ 0.7, i.e. skipping a meaningful fraction of
+negative-odor patches).
+
+## Metrics
+
+`LearningSetsMetrics`: `total_water_consumed`, `n_patches_seen`, `n_patches_visited`,
+`last_stop_duration_offset_updater`, `last_stop_velocity_threshold_updater`,
+`last_n_neg_sites_per_pair`, `last_reward_amount`.
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/__init__.py b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/__init__.py
new file mode 100644
index 00000000..cdab0921
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/__init__.py
@@ -0,0 +1,9 @@
+from .curriculum import CURRICULUM, CURRICULUM_NAME, PKG_LOCATION, TRAINER, run_curriculum
+
+__all__ = [
+    "CURRICULUM_NAME",
+    "CURRICULUM",
+    "TRAINER",
+    "run_curriculum",
+    "PKG_LOCATION",
+]
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/curriculum.py b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/curriculum.py
new file mode 100644
index 00000000..a60c82ee
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/curriculum.py
@@ -0,0 +1,72 @@
+from typing import Any, Type, TypeVar
+
+import aind_behavior_curriculum
+import pydantic
+from aind_behavior_curriculum import StageTransition, Trainer, TrainerState, create_curriculum
+from aind_behavior_vr_foraging.task_logic import AindVrForagingTaskLogic
+
+from .. import __semver__
+from ..cli import CurriculumCliArgs, CurriculumSuggestion
+from ..utils import metrics_from_dataset_path, trainer_state_from_file
+from .helpers import N_SITES_EACH, VELOCITY_THRESHOLD_FLOOR
+from .metrics import LearningSetsMetrics
+from .stages import make_s_graduated, make_s_shaping
+
+CURRICULUM_NAME = "LearningSets"
+PKG_LOCATION = ".".join(__name__.split(".")[:-1])
+
+TModel = TypeVar("TModel", bound=pydantic.BaseModel)
+
+
+# ============================================================
+# Stage transitions
+# ============================================================
+
+
+def st_shaping_to_graduated(metrics: LearningSetsMetrics) -> bool:
+    """Graduate the shaping stage once the negative-site proportion has reached the full
+    5+5 ratio, the stop-velocity threshold has been shaped down to its final floor, and
+    the subject is discriminating (skipping a meaningful fraction of patches)."""
+    if (
+        metrics.last_n_neg_sites_per_pair is None
+        or metrics.last_stop_velocity_threshold_updater is None
+        or metrics.n_patches_seen == 0
+    ):
+        return False
+    visit_ratio = metrics.n_patches_visited / metrics.n_patches_seen
+    return (
+        (metrics.last_n_neg_sites_per_pair >= N_SITES_EACH)
+        and (metrics.last_stop_velocity_threshold_updater <= VELOCITY_THRESHOLD_FLOOR)
+        and (metrics.n_patches_seen >= 200)
+        and (visit_ratio <= 0.7)
+    )
+
+
+# ============================================================
+# Curriculum definition
+# ============================================================
+
+curriculum_class: Type[aind_behavior_curriculum.Curriculum[AindVrForagingTaskLogic]] = create_curriculum(
+    CURRICULUM_NAME, __semver__, (AindVrForagingTaskLogic,), pkg_location=PKG_LOCATION
+)
+CURRICULUM = curriculum_class()
+
+CURRICULUM.add_stage_transition(
+    make_s_shaping(),
+    make_s_graduated(),
+    StageTransition(st_shaping_to_graduated),
+)
+
+# ==============================================================================
+# Trainer
+# ==============================================================================
+
+TRAINER = Trainer(CURRICULUM)
+
+
+def run_curriculum(args: CurriculumCliArgs) -> CurriculumSuggestion[TrainerState[Any], Any]:
+    metrics: aind_behavior_curriculum.Metrics
+    trainer_state = trainer_state_from_file(args.input_trainer_state, TRAINER)
+    metrics = metrics_from_dataset_path(args.data_directory, trainer_state)
+    trainer_state = TRAINER.evaluate(trainer_state, metrics)
+    return CurriculumSuggestion(trainer_state=trainer_state, metrics=metrics, version=__semver__)
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/helpers.py b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/helpers.py
new file mode 100644
index 00000000..d9b99497
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/helpers.py
@@ -0,0 +1,298 @@
+import random
+from collections import deque
+from dataclasses import dataclass
+from typing import Optional
+
+from aind_behavior_services.task import distributions
+from aind_behavior_vr_foraging import task_logic
+
+
+@dataclass
+class CorridorGeometry:
+    inter_patch_min_length: float
+    inter_patch_mean_length: float
+    inter_patch_max_length: float
+    reward_site_length: float
+
+
+#: Number of distinct odors available to the learning-set generator.
+ODOR_COUNT: int = 7
+
+#: Reward volume (µL) range and starting default. Sessions begin at ``DEFAULT``; the
+#: graduated-stage water policy steers it within ``[MIN, MAX]``.
+REWARD_AMOUNT_UL_MAX: float = 8.0
+REWARD_AMOUNT_UL_MIN: float = 4.0
+REWARD_AMOUNT_UL_DEFAULT: float = 6.0
+
+#: Stop-duration shaping. The operant ``stop_duration`` is held at ``STOP_DURATION_BASE``
+#: and a ``STOP_DURATION_OFFSET`` updater ramps on top of it, so the effective stop
+#: grows ``STOP_DURATION_BASE -> STOP_DURATION_BASE + STOP_DURATION_OFFSET_MAX`` (= 3.0 s).
+#: The base is deliberately tiny so that on the first few days (before the offset has
+#: accumulated / seeded) a brief dip below the velocity threshold counts as a stop and
+#: the subject can earn reward by chance.
+STOP_DURATION_BASE: float = 0.1
+STOP_DURATION_OFFSET_MAX: float = 2.9
+STOP_DURATION_GRADUATED: float = 3.0  # fixed stop in the graduated stage (no updater)
+
+#: Corridor geometry. Single reward site per patch; inter-site spacing is fixed. The
+#: inter-patch spacing and reward-site length are *shaped* across sessions by
+#: ``p_ease_geometry`` -- from the compressed (easy, day-1) values toward full spacing,
+#: scaled by how many sites the subject traveled in the prior session. Full spacing is
+#: reached at ``GEOMETRY_EASE_SITES`` traveled sites (``n_patches_seen``).
+INTERSITE_LENGTH: float = 15.0
+GEOMETRY_EASE_SITES: float = 150.0
+GEOMETRY_COMPRESSED: CorridorGeometry = CorridorGeometry(
+    inter_patch_min_length=30.0,
+    inter_patch_mean_length=40.0,
+    inter_patch_max_length=70.0,
+    reward_site_length=25.0,
+)
+GEOMETRY_FULL: CorridorGeometry = CorridorGeometry(
+    inter_patch_min_length=30.0,
+    inter_patch_mean_length=60.0,
+    inter_patch_max_length=190.0,
+    reward_site_length=40.0,
+)
+
+#: Stop-velocity-threshold shaping (cm/s). One GAIN updater drives the threshold from
+#: lenient (``VELOCITY_THRESHOLD_START``, so almost any deceleration registers as a stop)
+#: down to the final ``VELOCITY_THRESHOLD_FLOOR`` across sessions.
+#: ``STOP_VELOCITY_LEARNING_FACTOR`` eases each session's start a little above where the
+#: prior session floored.
+VELOCITY_THRESHOLD_START: float = 60.0
+VELOCITY_THRESHOLD_FLOOR: float = 8.0
+STOP_VELOCITY_LEARNING_FACTOR: float = 1.2
+
+#: Reward delay (s) between a qualifying stop and reward delivery. Kept fixed/simple.
+REWARD_DELAY_S: float = 0.5
+
+#: Daily sequence size: ``N_PAIRS`` (neg, pos) pairs, each repeated ``N_SITES_EACH``
+#: times for both variants -> ``N_PAIRS * N_SITES_EACH * 2`` reward sites per session.
+N_PAIRS: int = 1000
+N_SITES_EACH: int = 5
+
+#: Proportion-based discrimination ramp. Negative sites per pair are introduced
+#: progressively across sessions: session 1 → 0 neg (all positive), then 1, 3, 5.
+#: Once the ramp is exhausted the ratio stays at 5+5.
+N_NEG_RAMP: tuple[int, ...] = (1, 3, 5)
+
+#: Cross-session seeding: start the stop-duration offset a little below where the
+#: prior session ended, so the subject is not dropped at the longest stop on day N+1.
+STOP_DURATION_LEARNING_FACTOR: float = 0.85
+
+#: Graduated-stage water budget. If the prior session's water exceeds ``WATER_CAP_ML`` the
+#: reward amount is trimmed by ``REWARD_AMOUNT_STEP_UL``; if it falls below ``WATER_FLOOR_ML``
+#: the amount is raised by the same step (both clamped to the allowed range).
+WATER_CAP_ML: float = 1.0
+WATER_FLOOR_ML: float = 0.7
+REWARD_AMOUNT_STEP_UL: float = 0.5
+
+
+def clamp(value: float, minimum: float, maximum: float) -> float:
+    return max(minimum, min(value, maximum))
+
+
+def lerp(start: float, end: float, fraction: float) -> float:
+    return start + (end - start) * fraction
+
+
+def odor_concentration_from_index(odor_index: int, concentration: float = 1.0) -> list[float]:
+    """One-hot odor mixture vector for ``odor_index`` at ``concentration``."""
+    arr = [0.0 for _ in range(ODOR_COUNT)]
+    arr[odor_index] = concentration
+    return arr
+
+
+def get_odor_sequence(total_trials: int, n: int = 1) -> list[tuple[int, int]]:
+    """Generate ``total_trials`` (negative, positive) odor-index pairs.
+
+    Same rules as ``examples/task_learning_sets.py``: no odor that appeared in the
+    last ``n`` pairs may reappear, so consecutive pairs share no odor.
+    """
+    odors = list(range(ODOR_COUNT))
+    if len(odors) < 2 * n + 2:
+        raise ValueError("Not enough odors to satisfy the constraints with the given n.")
+
+    pairs: list[tuple[int, int]] = []
+    history: deque[tuple[int, int]] = deque(maxlen=n)
+    for _ in range(total_trials):
+        forbidden: set[int] = set()
+        for pair in history:
+            forbidden.update(pair)
+        available = [o for o in odors if o not in forbidden]
+        pos = random.choice(available)
+        available.remove(pos)
+        neg = random.choice(available)
+        pairs.append((neg, pos))
+        history.append((neg, pos))
+    return pairs
+
+
+def make_sequence(n_pos_each: int = N_SITES_EACH, n_neg_each: int = N_SITES_EACH, n_pairs: int = N_PAIRS) -> list[int]:
+    """Build the ``patch_indices`` ordering for one session's block.
+
+    For each (neg, pos) pair, emit ``n_neg_each`` negative-variant sites
+    (``state_index == neg``) and ``n_pos_each`` positive-variant sites
+    (``state_index == pos + ODOR_COUNT``), shuffled together.
+    """
+    trial_sequence: list[int] = []
+    for neg, pos in get_odor_sequence(total_trials=n_pairs, n=1):
+        block = [neg] * n_neg_each + [pos + ODOR_COUNT] * n_pos_each
+        random.shuffle(block)
+        trial_sequence.extend(block)
+    return trial_sequence
+
+
+def make_operation_control(velocity_threshold: float = VELOCITY_THRESHOLD_FLOOR) -> task_logic.OperationControl:
+    return task_logic.OperationControl(
+        position_control=task_logic.PositionControl(
+            frequency_filter_cutoff=5,
+            velocity_threshold=velocity_threshold,
+        ),
+    )
+
+
+def make_patch(
+    is_rewarded: bool,
+    odor_index: int,
+    p_reward: float,
+    reward_amount: float = REWARD_AMOUNT_UL_DEFAULT,
+    stop_duration: float = STOP_DURATION_BASE,
+    geometry: Optional[CorridorGeometry] = None,
+    inter_site_length: float = INTERSITE_LENGTH,
+) -> task_logic.Patch:
+    """A single odor-marked reward site (one reward site per patch).
+
+    ``is_rewarded`` selects the positive (``state_index = odor_index + ODOR_COUNT``)
+    or negative (``state_index = odor_index``) variant; ``p_reward`` sets its reward
+    probability (the curriculum shapes the negative variant's probability). The
+    inter-patch / reward-site lengths are the shaped geometry (see ``p_ease_geometry``).
+    """
+    if geometry is None:
+        geometry = GEOMETRY_FULL
+    return task_logic.Patch(
+        label=f"{odor_index}_{'Rewarded' if is_rewarded else 'NonRewarded'}",
+        state_index=odor_index + ODOR_COUNT * int(is_rewarded),
+        odor_specification=odor_concentration_from_index(odor_index, 1.0),
+        patch_terminators=[
+            task_logic.PatchTerminatorOnRewardSite(count=task_logic.scalar_value(1)),
+        ],
+        reward_specification=task_logic.RewardSpecification(
+            amount=task_logic.scalar_value(reward_amount),
+            probability=task_logic.scalar_value(p_reward),
+            delay=task_logic.scalar_value(REWARD_DELAY_S),
+            operant_logic=task_logic.OperantLogic(
+                is_operant=False,
+                stop_duration=stop_duration,
+            ),
+        ),
+        patch_virtual_sites_generator=task_logic.PatchVirtualSitesGenerator(
+            inter_patch=task_logic.VirtualSiteGenerator(
+                render_specification=task_logic.RenderSpecification(contrast=1),
+                label=task_logic.VirtualSiteLabels.INTERPATCH,
+                length_distribution=distributions.ExponentialDistribution(
+                    distribution_parameters=distributions.ExponentialDistributionParameters(
+                        rate=1 / geometry.inter_patch_mean_length
+                    ),
+                    scaling_parameters=distributions.ScalingParameters(offset=geometry.inter_patch_min_length),
+                    truncation_parameters=distributions.TruncationParameters(
+                        min=geometry.inter_patch_min_length,
+                        max=geometry.inter_patch_max_length,
+                    ),
+                ),
+            ),
+            inter_site=task_logic.VirtualSiteGenerator(
+                render_specification=task_logic.RenderSpecification(contrast=0.5),
+                label=task_logic.VirtualSiteLabels.INTERSITE,
+                length_distribution=task_logic.scalar_value(inter_site_length),
+            ),
+            reward_site=task_logic.VirtualSiteGenerator(
+                render_specification=task_logic.RenderSpecification(contrast=0.5),
+                label=task_logic.VirtualSiteLabels.REWARDSITE,
+                length_distribution=task_logic.scalar_value(geometry.reward_site_length),
+            ),
+        ),
+    )
+
+
+def make_block(
+    negative_probability: float,
+    positive_probability: float = 1.0,
+    reward_amount: float = REWARD_AMOUNT_UL_DEFAULT,
+    stop_duration: float = STOP_DURATION_BASE,
+    geometry: Optional[CorridorGeometry] = None,
+    n_pairs: int = N_PAIRS,
+    n_neg_each: int = 0,
+) -> task_logic.Block:
+    """One session's block: ``ODOR_COUNT`` negative + ``ODOR_COUNT`` positive odor patches
+    played in a freshly generated ``Ordered`` sequence.
+
+    ``n_neg_each`` controls how many negative-variant sites appear per pair (0 on day 1,
+    ramping via ``N_NEG_RAMP``); positive sites fill the remainder up to
+    ``N_SITES_EACH * 2`` total per pair. ``negative_probability`` sets the reward
+    probability for negative patches (fixed at 0 in the proportion-based design).
+    ``geometry`` defaults to the compressed day-1 values; ``p_ease_geometry`` eases them
+    toward full across sessions.
+    """
+    geometry = geometry if geometry is not None else GEOMETRY_COMPRESSED
+    n_pos_each = N_SITES_EACH * 2 - n_neg_each
+    patches = [
+        make_patch(
+            is_rewarded=False,
+            odor_index=i,
+            p_reward=negative_probability,
+            reward_amount=reward_amount,
+            stop_duration=stop_duration,
+            geometry=geometry,
+        )
+        for i in range(ODOR_COUNT)
+    ] + [
+        make_patch(
+            is_rewarded=True,
+            odor_index=i,
+            p_reward=positive_probability,
+            reward_amount=reward_amount,
+            stop_duration=stop_duration,
+            geometry=geometry,
+        )
+        for i in range(ODOR_COUNT)
+    ]
+    return task_logic.Block(
+        environment=task_logic.SequenceEnvironment(
+            patches=patches,
+            sampling_mode="Ordered",
+            patch_indices=make_sequence(n_pos_each=n_pos_each, n_neg_each=n_neg_each, n_pairs=n_pairs),
+        ),
+    )
+
+
+def make_stop_duration_updater(initial_value: float = 0.0) -> task_logic.NumericalUpdater:
+    """Within-session ramp of the stop-duration offset (0 -> ``STOP_DURATION_OFFSET_MAX``)."""
+    return task_logic.NumericalUpdater(
+        operation=task_logic.NumericalUpdaterOperation.OFFSET,
+        parameters=task_logic.NumericalUpdaterParameters(
+            initial_value=initial_value,
+            on_success=0.01,
+            minimum=0.0,
+            maximum=STOP_DURATION_OFFSET_MAX,
+        ),
+    )
+
+
+def make_stop_velocity_updater(
+    initial_value: float = VELOCITY_THRESHOLD_START,
+    minimum: float = VELOCITY_THRESHOLD_FLOOR,
+    maximum: float = VELOCITY_THRESHOLD_START,
+) -> task_logic.NumericalUpdater:
+    """Within-session shaping of the stop-velocity threshold: GAIN multiplies it by 0.93
+    on each rewarded stop, drawing it from ``initial_value`` down toward ``minimum`` so
+    the velocity slack closes as the subject practices real stops."""
+    return task_logic.NumericalUpdater(
+        operation=task_logic.NumericalUpdaterOperation.GAIN,
+        parameters=task_logic.NumericalUpdaterParameters(
+            initial_value=initial_value,
+            on_success=0.93,
+            minimum=minimum,
+            maximum=maximum,
+        ),
+    )
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/metrics.py b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/metrics.py
new file mode 100644
index 00000000..91446843
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/metrics.py
@@ -0,0 +1,88 @@
+import logging
+import os
+from typing import Optional
+
+import pandas as pd
+from aind_behavior_curriculum import Metrics
+from aind_behavior_vr_foraging.data_contract import dataset as vr_foraging_dataset
+from aind_behavior_vr_foraging.task_logic import AindVrForagingTaskLogic
+from contraqctor.contract.json import SoftwareEvents
+from pydantic import Field, NonNegativeFloat, NonNegativeInt
+
+from .helpers import N_PAIRS, ODOR_COUNT
+
+logger = logging.getLogger(__name__)
+
+
+class LearningSetsMetrics(Metrics):
+    total_water_consumed: NonNegativeFloat = Field(description="Total water (mL) consumed in the session.")
+    n_patches_visited: NonNegativeInt = Field(description="Number of harvest attempts (choices) made.")
+    n_patches_seen: NonNegativeInt = Field(description="Number of patches encountered during the session.")
+    last_stop_duration_offset_updater: Optional[NonNegativeFloat] = Field(
+        default=None, description="Stop-duration offset at the end of the session."
+    )
+    last_stop_velocity_threshold_updater: Optional[NonNegativeFloat] = Field(
+        default=None, description="Stop-velocity threshold at the end of the session."
+    )
+    last_n_neg_sites_per_pair: Optional[int] = Field(
+        default=None, description="Number of negative-variant sites per odor pair in the prior session's sequence."
+    )
+    last_reward_amount: Optional[NonNegativeFloat] = Field(
+        default=None, description="Reward amount (uL) in the prior session's task logic."
+    )
+
+
+def _try_get_datastream_as_dataframe(datastream: SoftwareEvents) -> pd.DataFrame | None:
+    try:
+        datastream.load()
+        return datastream.data
+    except FileNotFoundError:
+        return None
+
+
+def _scalar(value) -> Optional[float]:
+    """Best-effort extraction of a scalar from a (possibly ``Scalar``-distribution) field."""
+    try:
+        return float(value.distribution_parameters.value)
+    except AttributeError:
+        return None
+
+
+def metrics_from_dataset(data_directory: os.PathLike) -> LearningSetsMetrics:
+    dataset = vr_foraging_dataset(data_directory)
+
+    task_logic = dataset["Behavior"]["InputSchemas"]["TaskLogic"].load().data
+    if isinstance(task_logic, dict):
+        task_logic = AindVrForagingTaskLogic.model_validate(task_logic)
+
+    patches = task_logic.task_parameters.environment.blocks[0].environment.patches
+    patch_indices = task_logic.task_parameters.environment.blocks[0].environment.patch_indices
+    # Negative-odor variants have state_index < ODOR_COUNT.
+    n_neg_total = sum(1 for idx in patch_indices if idx < ODOR_COUNT)
+    last_n_neg_sites_per_pair = n_neg_total // N_PAIRS if patch_indices else None
+    last_reward_amount = _scalar(patches[0].reward_specification.amount) if patches else None
+
+    total_water_consumed = _try_get_datastream_as_dataframe(dataset["Behavior"]["SoftwareEvents"]["GiveReward"])
+    choices = _try_get_datastream_as_dataframe(dataset["Behavior"]["SoftwareEvents"]["ChoiceFeedback"])
+    visited_patches = _try_get_datastream_as_dataframe(dataset["Behavior"]["SoftwareEvents"]["ActivePatch"])
+    stop_duration_offset = _try_get_datastream_as_dataframe(
+        dataset["Behavior"]["SoftwareEvents"]["UpdaterStopDurationOffset"]
+    )
+    stop_velocity_threshold = _try_get_datastream_as_dataframe(
+        dataset["Behavior"]["SoftwareEvents"]["UpdaterStopVelocityThreshold"]
+    )
+
+    return LearningSetsMetrics(
+        total_water_consumed=(total_water_consumed["data"].sum() if total_water_consumed is not None else 0.0)
+        * 1e-3,  # uL -> mL
+        n_patches_visited=len(choices) if choices is not None else 0,
+        n_patches_seen=len(visited_patches) if visited_patches is not None else 0,
+        last_stop_duration_offset_updater=stop_duration_offset["data"].iloc[-1]
+        if stop_duration_offset is not None
+        else None,
+        last_stop_velocity_threshold_updater=stop_velocity_threshold["data"].iloc[-1]
+        if stop_velocity_threshold is not None
+        else None,
+        last_n_neg_sites_per_pair=last_n_neg_sites_per_pair,
+        last_reward_amount=last_reward_amount,
+    )
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/policies.py b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/policies.py
new file mode 100644
index 00000000..7206635b
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/policies.py
@@ -0,0 +1,121 @@
+from typing import Callable
+
+from aind_behavior_services.task import distributions
+from aind_behavior_vr_foraging import task_logic
+from aind_behavior_vr_foraging.task_logic import AindVrForagingTaskLogic
+
+from . import helpers
+from .metrics import LearningSetsMetrics
+
+PolicyType = Callable[[LearningSetsMetrics, AindVrForagingTaskLogic], AindVrForagingTaskLogic]
+
+
+def _iter_patches(task: AindVrForagingTaskLogic):
+    for block in task.task_parameters.environment.blocks:
+        for patch in block.environment.patches:
+            yield patch
+
+
+def p_introduce_negative_sites(metrics: LearningSetsMetrics, task: AindVrForagingTaskLogic) -> AindVrForagingTaskLogic:
+    """Progress the negative-site proportion according to ``N_NEG_RAMP`` and regenerate
+    the session sequence.
+
+    Session 1 (no prior metrics): n_neg_each = 0 — all sites are rewarded.
+    Subsequent sessions: walk 0 → 1 → 3 → 5 using ``N_NEG_RAMP``, then stay at 5.
+    Total sites per pair is always ``N_SITES_EACH * 2``; positive sites fill the
+    remainder.  The ramp is read from ``last_n_neg_sites_per_pair``; None means
+    session 1 (pre-ramp).
+    """
+    prior = metrics.last_n_neg_sites_per_pair
+    if prior is None:
+        n_neg_each = 0
+    else:
+        ramp = helpers.N_NEG_RAMP
+        # Advance to the next step in the ramp, or stay at the last step
+        idx = next((i for i, v in enumerate(ramp) if v > prior), len(ramp) - 1)
+        n_neg_each = ramp[idx]
+
+    n_pos_each = helpers.N_SITES_EACH * 2 - n_neg_each
+    for block in task.task_parameters.environment.blocks:
+        environment = block.environment
+        if isinstance(environment, task_logic.SequenceEnvironment):
+            environment.patch_indices = helpers.make_sequence(n_pos_each=n_pos_each, n_neg_each=n_neg_each)
+        else:
+            raise ValueError(f"Unexpected environment type {type(environment)}")
+    return task
+
+
+def p_seed_stop_duration(metrics: LearningSetsMetrics, task: AindVrForagingTaskLogic) -> AindVrForagingTaskLogic:
+    """Seed this session's stop-duration offset from the prior session's end value,
+    eased down (``STOP_DURATION_LEARNING_FACTOR``) so the subject re-ramps from a little
+    below the longest stop it reached rather than starting at the top."""
+    if metrics.last_stop_duration_offset_updater is None:
+        return task
+    updater = task.task_parameters.updaters.get(task_logic.UpdaterTarget.STOP_DURATION_OFFSET, None)
+    if updater is None:
+        return task
+    updater.parameters.initial_value = helpers.clamp(
+        metrics.last_stop_duration_offset_updater * helpers.STOP_DURATION_LEARNING_FACTOR,
+        minimum=updater.parameters.minimum,
+        maximum=updater.parameters.maximum,
+    )
+    return task
+
+
+def p_seed_stop_velocity(metrics: LearningSetsMetrics, task: AindVrForagingTaskLogic) -> AindVrForagingTaskLogic:
+    """Seed this session's stop-velocity threshold from the prior session's floored
+    value, eased up slightly (``STOP_VELOCITY_LEARNING_FACTOR``) so the subject does not
+    start straight at the floor. Once the threshold has asymptoted at the floor this is
+    effectively a no-op, so the policy can simply be dropped from later stages."""
+    if metrics.last_stop_velocity_threshold_updater is None:
+        return task
+    updater = task.task_parameters.updaters.get(task_logic.UpdaterTarget.STOP_VELOCITY_THRESHOLD, None)
+    if updater is None:
+        return task
+    updater.parameters.initial_value = helpers.clamp(
+        metrics.last_stop_velocity_threshold_updater * helpers.STOP_VELOCITY_LEARNING_FACTOR,
+        minimum=updater.parameters.minimum,
+        maximum=updater.parameters.maximum,
+    )
+    return task
+
+
+def p_ease_geometry(metrics: LearningSetsMetrics, task: AindVrForagingTaskLogic) -> AindVrForagingTaskLogic:
+    """Ease the corridor geometry from the compressed (day-1, easy) values toward full
+    spacing, scaled by how many sites the subject travelled in the prior session
+    (``n_patches_seen / GEOMETRY_EASE_SITES``, clamped to 1). On the first session (no
+    prior metrics) the geometry stays compressed. Inter-site spacing is left fixed."""
+    fraction = helpers.clamp(metrics.n_patches_seen / helpers.GEOMETRY_EASE_SITES, 0.0, 1.0)
+    compressed, full = helpers.GEOMETRY_COMPRESSED, helpers.GEOMETRY_FULL
+    inter_patch_min = helpers.lerp(compressed.inter_patch_min_length, full.inter_patch_min_length, fraction)
+    inter_patch_mean = helpers.lerp(compressed.inter_patch_mean_length, full.inter_patch_mean_length, fraction)
+    inter_patch_max = helpers.lerp(compressed.inter_patch_max_length, full.inter_patch_max_length, fraction)
+    reward_site_length = helpers.lerp(compressed.reward_site_length, full.reward_site_length, fraction)
+    for patch in _iter_patches(task):
+        generator = patch.patch_virtual_sites_generator
+        inter_patch_distribution = generator.inter_patch.length_distribution
+        if isinstance(inter_patch_distribution, distributions.ExponentialDistribution):
+            inter_patch_distribution.distribution_parameters.rate = 1.0 / inter_patch_mean
+            if inter_patch_distribution.scaling_parameters is not None:
+                inter_patch_distribution.scaling_parameters.offset = inter_patch_min
+            if inter_patch_distribution.truncation_parameters is not None:
+                inter_patch_distribution.truncation_parameters.min = inter_patch_min
+                inter_patch_distribution.truncation_parameters.max = inter_patch_max
+        generator.reward_site.length_distribution.distribution_parameters.value = reward_site_length
+    return task
+
+
+def p_water_cap(metrics: LearningSetsMetrics, task: AindVrForagingTaskLogic) -> AindVrForagingTaskLogic:
+    """Steer reward volume toward the target water window ``[WATER_FLOOR_ML, WATER_CAP_ML]``.
+    Trims by ``REWARD_AMOUNT_STEP_UL`` when the prior session exceeded the cap; raises by
+    the same step when it fell below the floor. Both directions are clamped to
+    ``[REWARD_AMOUNT_UL_MIN, REWARD_AMOUNT_UL_MAX]``."""
+    amount = metrics.last_reward_amount if metrics.last_reward_amount is not None else helpers.REWARD_AMOUNT_UL_DEFAULT
+    if metrics.total_water_consumed > helpers.WATER_CAP_ML:
+        amount = amount - helpers.REWARD_AMOUNT_STEP_UL
+    elif metrics.total_water_consumed < helpers.WATER_FLOOR_ML:
+        amount = amount + helpers.REWARD_AMOUNT_STEP_UL
+    amount = helpers.clamp(amount, helpers.REWARD_AMOUNT_UL_MIN, helpers.REWARD_AMOUNT_UL_MAX)
+    for patch in _iter_patches(task):
+        patch.reward_specification.amount = task_logic.scalar_value(amount)
+    return task
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/stages.py b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/stages.py
new file mode 100644
index 00000000..e40efa83
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/src/aind_behavior_vr_foraging_curricula/learning_sets/stages.py
@@ -0,0 +1,81 @@
+from aind_behavior_curriculum import MetricsProvider, Policy, Stage
+from aind_behavior_vr_foraging import task_logic
+from aind_behavior_vr_foraging.task_logic import AindVrForagingTaskLogic, AindVrForagingTaskParameters
+
+from . import helpers
+from .metrics import metrics_from_dataset
+from .policies import (
+    p_ease_geometry,
+    p_introduce_negative_sites,
+    p_seed_stop_duration,
+    p_seed_stop_velocity,
+    p_water_cap,
+)
+
+
+def make_s_shaping() -> Stage:
+    """The single shaping stage. Everything is shaped here by cross-session policies:
+
+    * speed -- the stop-velocity threshold ramps ``60 -> 8`` cm/s within session, seeded
+      across days;
+    * geometry -- ``p_ease_geometry`` eases inter-patch / reward-site lengths from the
+      compressed (easy) values toward full, scaled by sites travelled;
+    * negative proportion -- starts at 0 neg sites (all positive), then ramps 1 → 3 → 5
+      per pair via ``p_introduce_negative_sites``; negative patches are always p=0;
+    * stop duration -- ramps from a tiny base toward 3 s, seeded across days.
+    """
+    return Stage(
+        name="shaping",
+        task=AindVrForagingTaskLogic(
+            stage_name="shaping",
+            task_parameters=AindVrForagingTaskParameters(
+                rng_seed=None,
+                updaters={
+                    task_logic.UpdaterTarget.STOP_VELOCITY_THRESHOLD: helpers.make_stop_velocity_updater(),
+                    task_logic.UpdaterTarget.STOP_DURATION_OFFSET: helpers.make_stop_duration_updater(),
+                },
+                environment=task_logic.BlockStructure(
+                    blocks=[helpers.make_block(negative_probability=0.0, positive_probability=1.0, n_neg_each=0)],
+                    sampling_mode="Sequential",
+                ),
+                operation_control=helpers.make_operation_control(velocity_threshold=helpers.VELOCITY_THRESHOLD_START),
+            ),
+        ),
+        start_policies=[
+            Policy(p_introduce_negative_sites),
+            Policy(p_seed_stop_velocity),
+            Policy(p_seed_stop_duration),
+            Policy(p_ease_geometry),
+        ],
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
+
+
+def make_s_graduated() -> Stage:
+    """Negative odor fixed at 0%, stop duration fixed at 3 s, velocity fixed at the floor,
+    geometry at full spacing, full 5+5 site ratio (no updaters). The water-cap policy
+    trims reward volume across days if the prior session over-delivered water."""
+    return Stage(
+        name="graduated",
+        task=AindVrForagingTaskLogic(
+            stage_name="graduated",
+            task_parameters=AindVrForagingTaskParameters(
+                rng_seed=None,
+                environment=task_logic.BlockStructure(
+                    blocks=[
+                        helpers.make_block(
+                            negative_probability=0.0,
+                            positive_probability=1.0,
+                            stop_duration=helpers.STOP_DURATION_GRADUATED,
+                            geometry=helpers.GEOMETRY_FULL,
+                            n_neg_each=helpers.N_SITES_EACH,
+                        )
+                    ],
+                    sampling_mode="Sequential",
+                ),
+                operation_control=helpers.make_operation_control(velocity_threshold=helpers.VELOCITY_THRESHOLD_FLOOR),
+            ),
+        ),
+        start_policies=[Policy(p_introduce_negative_sites), Policy(p_water_cap)],
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/tests/learning_sets/__init__.py b/src/packages/aind_behavior_vr_foraging_curricula/tests/learning_sets/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/packages/aind_behavior_vr_foraging_curricula/tests/learning_sets/test_curriculum.py b/src/packages/aind_behavior_vr_foraging_curricula/tests/learning_sets/test_curriculum.py
new file mode 100644
index 00000000..39d3c886
--- /dev/null
+++ b/src/packages/aind_behavior_vr_foraging_curricula/tests/learning_sets/test_curriculum.py
@@ -0,0 +1,356 @@
+from typing import Any, cast
+
+import pytest
+from aind_behavior_curriculum import Curriculum, Trainer, TrainerState
+from aind_behavior_services.task import distributions
+from aind_behavior_vr_foraging.task_logic import AindVrForagingTaskLogic
+
+from aind_behavior_vr_foraging_curricula.learning_sets import CURRICULUM, TRAINER
+from aind_behavior_vr_foraging_curricula.learning_sets.curriculum import st_shaping_to_graduated
+from aind_behavior_vr_foraging_curricula.learning_sets.helpers import (
+    GEOMETRY_COMPRESSED,
+    GEOMETRY_FULL,
+    N_NEG_RAMP,
+    N_SITES_EACH,
+    ODOR_COUNT,
+    REWARD_AMOUNT_UL_DEFAULT,
+    REWARD_AMOUNT_UL_MAX,
+    REWARD_AMOUNT_UL_MIN,
+    STOP_DURATION_LEARNING_FACTOR,
+    STOP_VELOCITY_LEARNING_FACTOR,
+    VELOCITY_THRESHOLD_FLOOR,
+    VELOCITY_THRESHOLD_START,
+)
+from aind_behavior_vr_foraging_curricula.learning_sets.metrics import LearningSetsMetrics
+from aind_behavior_vr_foraging_curricula.learning_sets.policies import (
+    p_ease_geometry,
+    p_introduce_negative_sites,
+    p_seed_stop_duration,
+    p_seed_stop_velocity,
+    p_water_cap,
+)
+from aind_behavior_vr_foraging_curricula.learning_sets.stages import make_s_graduated, make_s_shaping
+
+
+@pytest.fixture
+def trainer() -> Trainer[Any]:
+    return TRAINER
+
+
+@pytest.fixture
+def curriculum() -> Curriculum[AindVrForagingTaskLogic]:
+    return CURRICULUM
+
+
+@pytest.fixture
+def init_state() -> TrainerState[Any]:
+    return TRAINER.create_enrollment()
+
+
+def _make_metrics(**overrides: Any) -> LearningSetsMetrics:
+    defaults: dict[str, Any] = dict(
+        total_water_consumed=0.0,
+        n_patches_visited=0,
+        n_patches_seen=0,
+        last_stop_duration_offset_updater=None,
+        last_stop_velocity_threshold_updater=None,
+        last_n_neg_sites_per_pair=None,
+        last_reward_amount=None,
+    )
+    defaults.update(overrides)
+    return LearningSetsMetrics(**defaults)
+
+
+def _task(stage_factory) -> AindVrForagingTaskLogic:
+    return cast(AindVrForagingTaskLogic, stage_factory().task).model_copy(deep=True)
+
+
+def _unique(values: list[float]) -> float:
+    assert len(set(values)) == 1, f"expected a single distinct value, got {set(values)}"
+    return values[0]
+
+
+def _negative_probability(task: AindVrForagingTaskLogic) -> float:
+    return _unique(
+        [
+            p.reward_specification.probability.distribution_parameters.value
+            for p in task.task_parameters.environment.blocks[0].environment.patches
+            if p.state_index < ODOR_COUNT
+        ]
+    )
+
+
+def _positive_probability(task: AindVrForagingTaskLogic) -> float:
+    return _unique(
+        [
+            p.reward_specification.probability.distribution_parameters.value
+            for p in task.task_parameters.environment.blocks[0].environment.patches
+            if p.state_index >= ODOR_COUNT
+        ]
+    )
+
+
+def _reward_amount(task: AindVrForagingTaskLogic) -> float:
+    return _unique(
+        [
+            p.reward_specification.amount.distribution_parameters.value
+            for p in task.task_parameters.environment.blocks[0].environment.patches
+        ]
+    )
+
+
+def _reward_site_length(task: AindVrForagingTaskLogic) -> float:
+    return _unique(
+        [
+            p.patch_virtual_sites_generator.reward_site.length_distribution.distribution_parameters.value
+            for p in task.task_parameters.environment.blocks[0].environment.patches
+        ]
+    )
+
+
+def _inter_patch_max(task: AindVrForagingTaskLogic) -> float:
+    return _unique(
+        [
+            p.patch_virtual_sites_generator.inter_patch.length_distribution.truncation_parameters.max
+            for p in task.task_parameters.environment.blocks[0].environment.patches
+        ]
+    )
+
+
+def _n_neg_per_pair(task: AindVrForagingTaskLogic) -> int:
+    from aind_behavior_vr_foraging_curricula.learning_sets.helpers import N_PAIRS
+
+    patch_indices = task.task_parameters.environment.blocks[0].environment.patch_indices
+    n_neg = sum(1 for idx in patch_indices if idx < ODOR_COUNT)
+    return n_neg // N_PAIRS
+
+
+# ============================================================
+# Stage-transition predicate
+# ============================================================
+
+
+class TestShapingToGraduated:
+    def _passing(self) -> LearningSetsMetrics:
+        return _make_metrics(
+            last_n_neg_sites_per_pair=N_SITES_EACH,
+            last_stop_velocity_threshold_updater=VELOCITY_THRESHOLD_FLOOR,
+            n_patches_seen=300,
+            n_patches_visited=150,
+        )
+
+    def test_pass(self):
+        assert st_shaping_to_graduated(self._passing()) is True
+
+    def test_fail_when_neg_ratio_not_full(self):
+        assert st_shaping_to_graduated(self._passing().model_copy(update=dict(last_n_neg_sites_per_pair=3))) is False
+
+    def test_fail_when_velocity_not_at_final_floor(self):
+        assert (
+            st_shaping_to_graduated(self._passing().model_copy(update=dict(last_stop_velocity_threshold_updater=30.0)))
+            is False
+        )
+
+    def test_fail_when_not_discriminating(self):
+        # 290/300 ~ 0.97 > 0.7 -> harvesting everything
+        assert st_shaping_to_graduated(self._passing().model_copy(update=dict(n_patches_visited=290))) is False
+
+    def test_fail_when_metric_none(self):
+        assert st_shaping_to_graduated(self._passing().model_copy(update=dict(last_n_neg_sites_per_pair=None))) is False
+
+
+# ============================================================
+# Policies
+# ============================================================
+
+
+class TestPIntroduceNegativeSites:
+    def test_first_session_no_negatives(self):
+        task = _task(make_s_shaping)
+        p_introduce_negative_sites(_make_metrics(), task)
+        assert _n_neg_per_pair(task) == 0
+
+    def test_ramp_step_0_to_1(self):
+        task = _task(make_s_shaping)
+        p_introduce_negative_sites(_make_metrics(last_n_neg_sites_per_pair=0), task)
+        assert _n_neg_per_pair(task) == N_NEG_RAMP[0]
+
+    def test_ramp_step_1_to_3(self):
+        task = _task(make_s_shaping)
+        p_introduce_negative_sites(_make_metrics(last_n_neg_sites_per_pair=1), task)
+        assert _n_neg_per_pair(task) == N_NEG_RAMP[1]
+
+    def test_ramp_step_3_to_5(self):
+        task = _task(make_s_shaping)
+        p_introduce_negative_sites(_make_metrics(last_n_neg_sites_per_pair=3), task)
+        assert _n_neg_per_pair(task) == N_NEG_RAMP[2]
+
+    def test_ramp_saturates_at_5(self):
+        task = _task(make_s_shaping)
+        p_introduce_negative_sites(_make_metrics(last_n_neg_sites_per_pair=5), task)
+        assert _n_neg_per_pair(task) == N_SITES_EACH
+
+    def test_indices_within_valid_range(self):
+        task = _task(make_s_shaping)
+        p_introduce_negative_sites(_make_metrics(last_n_neg_sites_per_pair=3), task)
+        indices = task.task_parameters.environment.blocks[0].environment.patch_indices
+        assert all(0 <= idx < 2 * ODOR_COUNT for idx in indices)
+
+
+class TestPSeedStopDuration:
+    def test_seeds_eased_lower(self):
+        task = _task(make_s_shaping)
+        p_seed_stop_duration(_make_metrics(last_stop_duration_offset_updater=2.0), task)
+        iv = task.task_parameters.updaters["StopDurationOffset"].parameters.initial_value
+        assert iv == pytest.approx(2.0 * STOP_DURATION_LEARNING_FACTOR)
+
+    def test_clamps_to_max(self):
+        task = _task(make_s_shaping)
+        p_seed_stop_duration(_make_metrics(last_stop_duration_offset_updater=1000.0), task)
+        params = task.task_parameters.updaters["StopDurationOffset"].parameters
+        assert params.initial_value == params.maximum
+
+    def test_no_op_when_metric_none(self):
+        task = _task(make_s_shaping)
+        original = task.task_parameters.updaters["StopDurationOffset"].parameters.initial_value
+        p_seed_stop_duration(_make_metrics(), task)
+        assert task.task_parameters.updaters["StopDurationOffset"].parameters.initial_value == original
+
+
+class TestPSeedStopVelocity:
+    def test_shaping_stage_runs_full_velocity_ramp(self):
+        task = _task(make_s_shaping)
+        params = task.task_parameters.updaters["StopVelocityThreshold"].parameters
+        assert params.initial_value == pytest.approx(VELOCITY_THRESHOLD_START)
+        assert params.minimum == pytest.approx(VELOCITY_THRESHOLD_FLOOR)
+        assert params.maximum == pytest.approx(VELOCITY_THRESHOLD_START)
+        assert task.task_parameters.operation_control.position_control.velocity_threshold == pytest.approx(
+            VELOCITY_THRESHOLD_START
+        )
+
+    def test_seeds_eased_above_floor(self):
+        task = _task(make_s_shaping)
+        p_seed_stop_velocity(_make_metrics(last_stop_velocity_threshold_updater=25.0), task)
+        iv = task.task_parameters.updaters["StopVelocityThreshold"].parameters.initial_value
+        assert iv == pytest.approx(25.0 * STOP_VELOCITY_LEARNING_FACTOR)
+
+    def test_clamps_to_max(self):
+        task = _task(make_s_shaping)
+        p_seed_stop_velocity(_make_metrics(last_stop_velocity_threshold_updater=1000.0), task)
+        params = task.task_parameters.updaters["StopVelocityThreshold"].parameters
+        assert params.initial_value == params.maximum
+
+    def test_no_op_when_metric_none(self):
+        task = _task(make_s_shaping)
+        original = task.task_parameters.updaters["StopVelocityThreshold"].parameters.initial_value
+        p_seed_stop_velocity(_make_metrics(), task)
+        assert task.task_parameters.updaters["StopVelocityThreshold"].parameters.initial_value == original
+
+    def test_no_velocity_updater_in_graduated_stage(self):
+        task = _task(make_s_graduated)
+        assert "StopVelocityThreshold" not in task.task_parameters.updaters
+        assert task.task_parameters.operation_control.position_control.velocity_threshold == pytest.approx(
+            VELOCITY_THRESHOLD_FLOOR
+        )
+
+
+class TestPLearnToRun:
+    def test_first_session_stays_compressed(self):
+        task = _task(make_s_shaping)
+        p_ease_geometry(_make_metrics(n_patches_seen=0), task)
+        assert _reward_site_length(task) == pytest.approx(GEOMETRY_COMPRESSED.reward_site_length)
+        assert _inter_patch_max(task) == pytest.approx(GEOMETRY_COMPRESSED.inter_patch_max_length)
+
+    def test_eases_to_full_after_enough_travel(self):
+        task = _task(make_s_shaping)
+        p_ease_geometry(_make_metrics(n_patches_seen=10_000), task)
+        assert _reward_site_length(task) == pytest.approx(GEOMETRY_FULL.reward_site_length)
+        assert _inter_patch_max(task) == pytest.approx(GEOMETRY_FULL.inter_patch_max_length)
+
+    def test_partial_easing_is_between(self):
+        task = _task(make_s_shaping)
+        from aind_behavior_vr_foraging_curricula.learning_sets.helpers import GEOMETRY_EASE_SITES
+
+        p_ease_geometry(_make_metrics(n_patches_seen=int(GEOMETRY_EASE_SITES // 2)), task)
+        midpoint = 0.5 * (GEOMETRY_COMPRESSED.reward_site_length + GEOMETRY_FULL.reward_site_length)
+        assert _reward_site_length(task) == pytest.approx(midpoint)
+
+    def test_inter_patch_distribution_stays_exponential(self):
+        task = _task(make_s_shaping)
+        p_ease_geometry(_make_metrics(n_patches_seen=10_000), task)
+        for patch in task.task_parameters.environment.blocks[0].environment.patches:
+            dist = patch.patch_virtual_sites_generator.inter_patch.length_distribution
+            assert isinstance(dist, distributions.ExponentialDistribution)
+
+
+class TestPWaterCap:
+    def test_trims_when_over_budget(self):
+        task = _task(make_s_graduated)
+        p_water_cap(_make_metrics(total_water_consumed=1.2, last_reward_amount=8.0), task)
+        assert _reward_amount(task) == pytest.approx(7.5)
+
+    def test_raises_when_under_floor(self):
+        task = _task(make_s_graduated)
+        p_water_cap(_make_metrics(total_water_consumed=0.5, last_reward_amount=5.0), task)
+        assert _reward_amount(task) == pytest.approx(5.5)
+
+    def test_holds_within_window(self):
+        task = _task(make_s_graduated)
+        p_water_cap(_make_metrics(total_water_consumed=0.7, last_reward_amount=6.0), task)
+        assert _reward_amount(task) == pytest.approx(6.0)
+
+    def test_never_below_min(self):
+        task = _task(make_s_graduated)
+        p_water_cap(_make_metrics(total_water_consumed=0.9, last_reward_amount=REWARD_AMOUNT_UL_MIN), task)
+        assert _reward_amount(task) == pytest.approx(REWARD_AMOUNT_UL_MIN)
+
+    def test_never_above_max(self):
+        task = _task(make_s_graduated)
+        p_water_cap(_make_metrics(total_water_consumed=0.5, last_reward_amount=REWARD_AMOUNT_UL_MAX), task)
+        assert _reward_amount(task) == pytest.approx(REWARD_AMOUNT_UL_MAX)
+
+    def test_defaults_to_default_when_metric_none(self):
+        task = _task(make_s_graduated)
+        p_water_cap(_make_metrics(total_water_consumed=0.7), task)
+        assert _reward_amount(task) == pytest.approx(REWARD_AMOUNT_UL_DEFAULT)
+
+
+# ============================================================
+# Full progression
+# ============================================================
+
+
+class TestProgression:
+    def test_stage_set(self, trainer: Trainer):
+        assert {s.name for s in trainer.curriculum.see_stages()} == {"shaping", "graduated"}
+
+    def test_advance_to_graduated(self, trainer: Trainer, init_state: TrainerState):
+        state = init_state
+        assert state.stage is not None and state.stage.name == "shaping"
+
+        state = trainer.evaluate(
+            state,
+            _make_metrics(
+                last_n_neg_sites_per_pair=N_SITES_EACH,
+                last_stop_velocity_threshold_updater=VELOCITY_THRESHOLD_FLOOR,
+                n_patches_seen=300,
+                n_patches_visited=150,
+            ),
+        )
+        assert state.stage is not None and state.stage.name == "graduated"
+
+    def test_stays_on_shaping_until_full_ratio(self, trainer: Trainer, init_state: TrainerState):
+        state = trainer.evaluate(
+            init_state,
+            _make_metrics(
+                last_n_neg_sites_per_pair=3,
+                last_stop_velocity_threshold_updater=VELOCITY_THRESHOLD_FLOOR,
+                n_patches_seen=300,
+                n_patches_visited=200,
+            ),
+        )
+        assert state.stage is not None and state.stage.name == "shaping"
+
+    def test_no_advance_on_insufficient_metrics(self, trainer: Trainer, init_state: TrainerState):
+        state = trainer.evaluate(init_state, _make_metrics())
+        assert state.stage is not None and state.stage.name == "shaping"