diff --git a/README.md b/README.md
index edbf35f..30d3dc2 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 [![ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 [![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
 
-A repository for the Dynamic Foraging task.
+A repository for the Dynamic Foraging task and its associated curricula.
 
 ---
 
@@ -34,7 +34,7 @@ from the root of the repository.
 
 ## ⚙️ Generating settings files
 
-The Dynamic Foraging tasks is instantiated by a set of three settings files that strictly follow a DSL schema. These files are:
+The Dynamic Foraging task is instantiated by a set of three settings files that strictly follow a DSL schema. These files are:
 
 - `task_logic.json`
 - `rig.json`
@@ -52,6 +52,8 @@ However, for a better experiment management user experience, it is recommended t
 
 ## [> ] CLI tools
 
+### Task CLI
+
 The platform exposes a few CLI tools to facilitate various tasks. Tools are available via:
 
 ```powershell
@@ -66,6 +68,103 @@ uv run dynamic-foraging -h
 
 You may need to install optional dependencies depending on the sub-commands you run.
 
+### Curriculum CLI
+
+Curricula are available via the `curriculum` CLI entry point. For a full list of commands:
+
+```powershell
+uv run curriculum -h
+```
+
+#### `list` - List Available Curricula
+
+```bash
+uv run curriculum list
+```
+
+#### `init` - Initialize a Curriculum
+
+Creates an initial trainer state for enrolling a subject in a curriculum.
+
+```bash
+# Start at the first stage
+uv run curriculum init --curriculum coupled_baiting --output initial_state.json
+
+# Start at a specific stage
+uv run curriculum init --curriculum coupled_baiting --stage s_stage_1 --output initial_state.json
+```
+
+#### `run` - Run a Curriculum
+
+Evaluates a curriculum based on session data and current trainer state.
+
+```bash
+uv run curriculum run \
+  --data-directory /path/to/session/data \
+  --input-trainer-state current_state.json \
+  --output-suggestion /path/to/output
+```
+
+Force a specific curriculum:
+
+```bash
+uv run curriculum run \
+  --data-directory /path/to/session/data \
+  --input-trainer-state current_state.json \
+  --curriculum coupled_baiting \
+  --output-suggestion /path/to/output
+```
+
+#### `version` / `dsl-version` - Show Versions
+
+```bash
+uv run curriculum version      # Package version
+uv run curriculum dsl-version  # Underlying DSL library version
+```
+
+---
+
+## Typical curriculum workflow
+
+1. **List available curricula:**
+   ```bash
+   uv run curriculum list
+   ```
+
+2. **Initialize a subject:**
+   ```bash
+   uv run curriculum init --curriculum coupled_baiting --output trainer_state.json
+   ```
+
+3. **After a session, evaluate progress:**
+   ```bash
+   uv run curriculum run \
+     --data-directory /path/to/session/data \
+     --input-trainer-state trainer_state.json \
+     --output-suggestion /path/to/output
+   ```
+
+4. **Use the suggestion for the next session:**
+   The `suggestion.json` output can be passed as `--input-trainer-state` for the next session.
+
+---
+
+## Style guide
+
+To keep things clear, the following naming conventions are recommended:
+
+- **Policies** should start with `p_` (e.g., `p_identity_policy`)
+- **Policy transitions** should start with `pt_`
+- **Stages** should start with `s_` (e.g., `s_stage1`)
+- **Stage transitions** should start with `st_` and be named after the stages they transition between (e.g., `st_s_stage1_s_stage2`)
+
+Define the following modules within a curriculum:
+
+- **metrics**: Defines (or imports) metrics classes and how to calculate them from data
+- **stages**: Defines the different stages of the task, including task settings and optionally policies
+- **curriculum**: Defines transitions between stages and generates the entry point to the application
+
+---
 
 ## 🎮 Experiment launcher (temporarily CLABE)
 
diff --git a/pyproject.toml b/pyproject.toml
index 361205b..9e725b2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,6 +23,9 @@ dependencies = [
     "pydantic-settings",
 ]
 
+[tool.uv.workspace]
+members = ["workspace/*"]
+
 [project.urls]
 Documentation = "https://allenneuraldynamics.github.io/Aind.Behavior.DynamicForaging/"
 Repository = "https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging/"
diff --git a/schema/aind_behavior_dynamic_foraging.json b/schema/aind_behavior_dynamic_foraging.json
index 7a5bed9..435399c 100644
--- a/schema/aind_behavior_dynamic_foraging.json
+++ b/schema/aind_behavior_dynamic_foraging.json
@@ -63,7 +63,7 @@
           ]
         },
         "harp_lickometer_right": {
-          "defulat": null,
+          "default": null,
           "description": "Harp right lickometer",
           "oneOf": [
             {
@@ -121,7 +121,6 @@
         "data_directory",
         "triggered_camera_controller",
         "harp_behavior",
-        "harp_lickometer_right",
         "harp_clock_generator",
         "harp_sound_card",
         "manipulator",
@@ -1983,7 +1982,7 @@
       "type": "object"
     },
     "RewardProbabilityParameters": {
-      "description": "Defines the reward probability structure for a dynamic foraging task.\n\nReward probabilities are defined as pairs (p_left, p_right) normalized by\nbase_reward_sum. Pairs are drawn from a family representing a difficulty level:\n\n    Family 0:   [[8, 1], [6, 1], [3, 1], [1, 1]]\n    Family 1:  [[8, 1], [1, 1]]\n    Family 2:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]\n    Family 3:  [[6, 1], [3, 1], [1, 1]]",
+      "description": "Defines the reward probability structure for a dynamic foraging task.\n\nReward probabilities are defined as pairs (p_left, p_right) normalized by\nbase_reward_sum. Pairs are drawn from a family representing a difficulty level:\n\n    Family 1:   [[8, 1], [6, 1], [3, 1], [1, 1]]\n    Family 2:  [[8, 1], [1, 1]]\n    Family 3:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]\n    Family 4:  [[6, 1], [3, 1], [1, 1]]",
       "properties": {
         "base_reward_sum": {
           "default": 0.8,
@@ -3201,16 +3200,12 @@
         "block_len": {
           "$ref": "#/$defs/Distribution",
           "default": {
-            "family": "Exponential",
+            "family": "Scalar",
             "distribution_parameters": {
-              "family": "Exponential",
-              "rate": 1.0
-            },
-            "truncation_parameters": {
-              "max": 2.0,
-              "min": 1.0,
-              "truncation_mode": "exclude"
+              "family": "Scalar",
+              "value": 0.0
             },
+            "truncation_parameters": null,
             "scaling_parameters": null
           },
           "description": "Distribution describing block length."
diff --git a/scripts/walk_through_session.py b/scripts/walk_through_session.py
new file mode 100644
index 0000000..f6288ac
--- /dev/null
+++ b/scripts/walk_through_session.py
@@ -0,0 +1,37 @@
+import logging
+import os
+
+from aind_behavior_dynamic_foraging.data_contract import dataset as df_foraging_dataset
+from aind_behavior_dynamic_foraging.task_logic.trial_generators.warmup_trial_generator import WarmupTrialGeneratorSpec
+from aind_behavior_dynamic_foraging.task_logic.trial_models import TrialOutcome
+
+logging.basicConfig(
+    level=logging.DEBUG,
+)
+logger = logging.getLogger(__name__)
+
+
+def walk_through_session(data_directory: os.PathLike):
+    dataset = df_foraging_dataset(data_directory)
+    software_events = dataset["Behavior"]["SoftwareEvents"]
+    software_events.load_all()
+
+    trial_outcomes = software_events["TrialOutcome"].data["data"].iloc
+    warmup_trial_generator = WarmupTrialGeneratorSpec().create_generator()
+    for i, outcome in enumerate(trial_outcomes):
+        warmup_trial_generator.update(TrialOutcome.model_validate(outcome))
+        trial = warmup_trial_generator.next()
+
+        if not trial:
+            print(f"Session finished at trial {i}")
+            return
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Walk through a behavior session.")
+    parser.add_argument("--data-directory", help="Path to the session directory")
+    args = parser.parse_args()
+
+    walk_through_session(args.data_directory)
diff --git a/src/Extensions/AindBehaviorDynamicForaging.Generated.cs b/src/Extensions/AindBehaviorDynamicForaging.Generated.cs
index ef72cbe..3675f30 100644
--- a/src/Extensions/AindBehaviorDynamicForaging.Generated.cs
+++ b/src/Extensions/AindBehaviorDynamicForaging.Generated.cs
@@ -232,7 +232,7 @@ public HarpLicketySplit HarpLickometerLeft
         /// Harp right lickometer
         /// </summary>
         [System.Xml.Serialization.XmlIgnoreAttribute()]
-        [Newtonsoft.Json.JsonPropertyAttribute("harp_lickometer_right", Required=Newtonsoft.Json.Required.AllowNull)]
+        [Newtonsoft.Json.JsonPropertyAttribute("harp_lickometer_right")]
         [System.ComponentModel.DescriptionAttribute("Harp right lickometer")]
         public HarpLicketySplit HarpLickometerRight
         {
@@ -2939,10 +2939,10 @@ public override string ToString()
     ///Reward probabilities are defined as pairs (p_left, p_right) normalized by
     ///base_reward_sum. Pairs are drawn from a family representing a difficulty level:
     ///
-    ///    Family 0:   [[8, 1], [6, 1], [3, 1], [1, 1]]
-    ///    Family 1:  [[8, 1], [1, 1]]
-    ///    Family 2:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]
-    ///    Family 3:  [[6, 1], [3, 1], [1, 1]]
+    ///    Family 1:   [[8, 1], [6, 1], [3, 1], [1, 1]]
+    ///    Family 2:  [[8, 1], [1, 1]]
+    ///    Family 3:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]
+    ///    Family 4:  [[6, 1], [3, 1], [1, 1]]
     /// </summary>
     [System.CodeDom.Compiler.GeneratedCodeAttribute("Bonsai.Sgen", "0.9.0.0 (Newtonsoft.Json v13.0.0.0)")]
     [System.ComponentModel.DescriptionAttribute(@"Defines the reward probability structure for a dynamic foraging task.
@@ -2950,10 +2950,10 @@ public override string ToString()
     Reward probabilities are defined as pairs (p_left, p_right) normalized by
     base_reward_sum. Pairs are drawn from a family representing a difficulty level:
 
-        Family 0:   [[8, 1], [6, 1], [3, 1], [1, 1]]
-        Family 1:  [[8, 1], [1, 1]]
-        Family 2:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]
-        Family 3:  [[6, 1], [3, 1], [1, 1]]")]
+        Family 1:   [[8, 1], [6, 1], [3, 1], [1, 1]]
+        Family 2:  [[8, 1], [1, 1]]
+        Family 3:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]
+        Family 4:  [[6, 1], [3, 1], [1, 1]]")]
     [Bonsai.WorkflowElementCategoryAttribute(Bonsai.ElementCategory.Source)]
     [Bonsai.CombinatorAttribute(MethodName="Generate")]
     public partial class RewardProbabilityParameters
diff --git a/src/aind_behavior_dynamic_foraging/data_contract/_dataset.py b/src/aind_behavior_dynamic_foraging/data_contract/_dataset.py
index 69a73de..3f2e238 100644
--- a/src/aind_behavior_dynamic_foraging/data_contract/_dataset.py
+++ b/src/aind_behavior_dynamic_foraging/data_contract/_dataset.py
@@ -1,5 +1,6 @@
 from pathlib import Path
 
+from aind_behavior_curriculum import TrainerState
 from aind_behavior_services.session import Session
 from contraqctor.contract import Dataset, DataStreamCollection
 from contraqctor.contract.camera import Camera
@@ -7,7 +8,7 @@
     DeviceYmlByFile,
     HarpDevice,
 )
-from contraqctor.contract.json import PydanticModel, SoftwareEvents
+from contraqctor.contract.json import Json, PydanticModel, SoftwareEvents
 from contraqctor.contract.mux import MapFromPaths
 
 from .. import __semver__
@@ -58,6 +59,19 @@ def make_dataset(
                 name="Behavior",
                 description="Data from the Behavior modality",
                 data_streams=[
+                    Json(
+                        name="PreviousMetrics",
+                        reader_params=Json.make_params(
+                            path=root_path / "behavior/previous_metrics.json",
+                        ),
+                    ),
+                    PydanticModel(
+                        name="TrainerState",
+                        reader_params=PydanticModel.make_params(
+                            model=TrainerState,
+                            path=root_path / "behavior/trainer_state.json",
+                        ),
+                    ),
                     HarpDevice(
                         name="HarpBehavior",
                         reader_params=HarpDevice.make_params(
diff --git a/src/aind_behavior_dynamic_foraging/rig.py b/src/aind_behavior_dynamic_foraging/rig.py
index dbd964d..c1a94f0 100644
--- a/src/aind_behavior_dynamic_foraging/rig.py
+++ b/src/aind_behavior_dynamic_foraging/rig.py
@@ -72,7 +72,7 @@ class AindDynamicForagingRig(rig.Rig):
     )
     harp_behavior: harp.HarpBehavior = Field(description="Harp behavior")
     harp_lickometer_left: Optional[harp.HarpLicketySplit] = Field(default=None, description="Harp left lickometer")
-    harp_lickometer_right: Optional[harp.HarpLicketySplit] = Field(defulat=None, description="Harp right lickometer")
+    harp_lickometer_right: Optional[harp.HarpLicketySplit] = Field(default=None, description="Harp right lickometer")
     harp_clock_generator: harp.HarpWhiteRabbit = Field(description="Harp clock generator")
     harp_sound_card: DynamicForagingSoundCard = Field(description="Harp sound card")
     harp_sniff_detector: Optional[harp.HarpSniffDetector] = Field(default=None, description="Harp sniff detector")
diff --git a/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/block_based_trial_generator.py b/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/block_based_trial_generator.py
index 9788a0b..0112c95 100644
--- a/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/block_based_trial_generator.py
+++ b/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/block_based_trial_generator.py
@@ -26,10 +26,10 @@ class RewardProbabilityParameters(BaseModel):
     Reward probabilities are defined as pairs (p_left, p_right) normalized by
     base_reward_sum. Pairs are drawn from a family representing a difficulty level:
 
-        Family 0:   [[8, 1], [6, 1], [3, 1], [1, 1]]
-        Family 1:  [[8, 1], [1, 1]]
-        Family 2:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]
-        Family 3:  [[6, 1], [3, 1], [1, 1]]
+        Family 1:   [[8, 1], [6, 1], [3, 1], [1, 1]]
+        Family 2:  [[8, 1], [1, 1]]
+        Family 3:  [[1.0, 0.0], [0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]
+        Family 4:  [[6, 1], [3, 1], [1, 1]]
 
     """
 
diff --git a/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/warmup_trial_generator.py b/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/warmup_trial_generator.py
index e560b19..9f115cc 100644
--- a/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/warmup_trial_generator.py
+++ b/src/aind_behavior_dynamic_foraging/task_logic/trial_generators/warmup_trial_generator.py
@@ -1,12 +1,7 @@
 import logging
 from typing import Literal
 
-from aind_behavior_services.task.distributions import (
-    Distribution,
-    ExponentialDistribution,
-    ExponentialDistributionParameters,
-    TruncationParameters,
-)
+from aind_behavior_services.task.distributions import Distribution, Scalar
 from pydantic import BaseModel, Field
 
 from ..trial_models import TrialOutcome
@@ -41,10 +36,7 @@ class WarmupTrialGeneratorSpec(BlockBasedTrialGeneratorSpec):
     type: Literal["WarmupTrialGenerator"] = "WarmupTrialGenerator"
 
     block_len: Distribution = Field(
-        default=ExponentialDistribution(
-            distribution_parameters=ExponentialDistributionParameters(rate=1),
-            truncation_parameters=TruncationParameters(min=1, max=2),
-        ),
+        default=Scalar(value=1),
         description="Distribution describing block length.",
     )
 
@@ -69,7 +61,8 @@ def _are_end_conditions_met(self) -> bool:
         """
 
         end_conditions = self.spec.trial_generation_end_parameters
-        choice_history = self.is_right_choice_history
+        win = end_conditions.evaluation_window
+        choice_history = self.is_right_choice_history[-win:] if win > 0 else self.is_right_choice_history
 
         choice_len = len(choice_history)
         left_choices = choice_history.count(False)
@@ -78,20 +71,25 @@ def _are_end_conditions_met(self) -> bool:
 
         finish_ratio = 0 if choice_len == 0 else (unignored) / choice_len
         choice_ratio = 0 if unignored == 0 else right_choices / (unignored)
-
         if (
-            choice_len >= end_conditions.min_trial
+            len(self.is_right_choice_history) >= end_conditions.min_trial
             and finish_ratio >= end_conditions.min_response_rate
             and abs(choice_ratio - 0.5) <= end_conditions.max_choice_bias
         ):
             logger.debug(
                 "Warmup trial generation end conditions met: "
-                f"total trials={choice_len}, "
+                f"total trials={len(self.is_right_choice_history)}, "
                 f"finish ratio={finish_ratio}, "
                 f"choice bias={abs(choice_ratio - 0.5)}"
             )
             return True
 
+        logger.debug(
+            "Warmup trial generation end conditions are not met: "
+            f"total trials={len(self.is_right_choice_history)}, "
+            f"finish ratio={finish_ratio}, "
+            f"choice bias={abs(choice_ratio - 0.5)}"
+        )
         return False
 
     def update(self, outcome: TrialOutcome | str) -> None:
diff --git a/uv.lock b/uv.lock
index 17e112d..65f6e74 100644
--- a/uv.lock
+++ b/uv.lock
@@ -13,6 +13,12 @@ resolution-markers = [
     "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
+[manifest]
+members = [
+    "aind-behavior-dynamic-foraging",
+    "aind-behavior-dynamic-foraging-curricula",
+]
+
 [[package]]
 name = "accessible-pygments"
 version = "0.0.5"
@@ -27,16 +33,16 @@ wheels = [
 
 [[package]]
 name = "aind-behavior-curriculum"
-version = "0.0.37"
+version = "0.0.38"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jinja2" },
     { name = "pydantic" },
     { name = "semver" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f0/ac/cb5073f94c6b41b88c6e91d8bf41f7c140d659a0eb1191bc0a91cace60db/aind_behavior_curriculum-0.0.37.tar.gz", hash = "sha256:a6d8fd58b4d172655bc445eefefe8ba6a7966f2b4303afe138dce3c07ec45a13", size = 139105, upload-time = "2025-12-05T22:51:07.253Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/49/ad/812f8cd33857366d7b1da81443ed60777992e591438c7104ad9fe0d4ac9f/aind_behavior_curriculum-0.0.38.tar.gz", hash = "sha256:11cce6a455ee3a2c0464e5f0ff170c44f1f9caf50cd3f1bf9bd9df7a41248e0d", size = 139193, upload-time = "2026-03-09T10:56:32.906Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/83/c51f680b86136dca811816746568de0feadbbe03d27a3213500a744ff047/aind_behavior_curriculum-0.0.37-py3-none-any.whl", hash = "sha256:349e973d52a450523b03a4b73b8371f72c80e9d43702eb62005f250e51663ca7", size = 48014, upload-time = "2025-12-05T22:51:06.351Z" },
+    { url = "https://files.pythonhosted.org/packages/74/36/53daf76d7a3a27245fdee89e7900bbb13b9db0327af22d5f3e10c3273720/aind_behavior_curriculum-0.0.38-py3-none-any.whl", hash = "sha256:ec77803fc0cad1c9f430bad3f1d2404dd324ce24ee7970b8d8337de2d6ad2d3c", size = 48048, upload-time = "2026-03-09T10:56:31.299Z" },
 ]
 
 [[package]]
@@ -94,6 +100,55 @@ docs = [
     { name = "sphinx-jsonschema" },
 ]
 
+[[package]]
+name = "aind-behavior-dynamic-foraging-curricula"
+version = "0.2.1"
+source = { editable = "workspace/aind_behavior_dynamic_foraging_curricula" }
+dependencies = [
+    { name = "aind-behavior-curriculum" },
+    { name = "aind-behavior-dynamic-foraging" },
+    { name = "numpy" },
+    { name = "pydantic-settings" },
+]
+
+[package.dev-dependencies]
+dev = [
+    { name = "codespell" },
+    { name = "pytest" },
+    { name = "pytest-cov" },
+    { name = "ruff" },
+]
+docs = [
+    { name = "mkdocs" },
+    { name = "mkdocs-material" },
+    { name = "mkdocstrings", extra = ["python"] },
+    { name = "pymdown-extensions" },
+    { name = "ruff" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "aind-behavior-curriculum", specifier = ">=0.0.38" },
+    { name = "aind-behavior-dynamic-foraging", editable = "." },
+    { name = "numpy", specifier = ">=2.4.2" },
+    { name = "pydantic-settings" },
+]
+
+[package.metadata.requires-dev]
+dev = [
+    { name = "codespell" },
+    { name = "pytest" },
+    { name = "pytest-cov" },
+    { name = "ruff" },
+]
+docs = [
+    { name = "mkdocs" },
+    { name = "mkdocs-material" },
+    { name = "mkdocstrings", extras = ["python"] },
+    { name = "pymdown-extensions" },
+    { name = "ruff" },
+]
+
 [[package]]
 name = "aind-behavior-services"
 version = "0.13.5"
@@ -298,6 +353,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845, upload-time = "2026-02-01T12:30:53.445Z" },
 ]
 
+[[package]]
+name = "backrefs"
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+]
+
 [[package]]
 name = "wcwidth"
 version = "0.6.0"
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/README.md b/workspace/aind_behavior_dynamic_foraging_curricula/README.md
new file mode 100644
index 0000000..5744d03
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/README.md
@@ -0,0 +1,223 @@
+# Aind.Behavior.DynamicForaging.Curricula
+
+![CI](https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging.Curricula/actions/workflows/dynamic-foraging-curricula.yml/badge.svg)
+[![License](https://img.shields.io/badge/license-MIT-brightgreen)](LICENSE)
+[![ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
+
+
+A repository of curricula for [Dynamic foraging task](https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging).
+
+## CLI Reference
+
+Curricula are modules of the main package: `aind_behavior_dynamic_foraging_curricula.<curriculum_name>`. 
+
+All curricula are available via the `curriculum` CLI entry point. The following commands are available:
+
+### Getting Help
+
+Display all available commands:
+
+```bash
+uv run curriculum -h
+```
+
+Get help for a specific command:
+
+```bash
+uv run curriculum <command> -h
+```
+
+### `list` - List Available Curricula
+
+Lists all curricula available in this repository.
+
+**Usage:**
+
+```bash
+uv run curriculum list
+```
+
+**Example output:**
+```
+Available curricula:
+  coupled_baiting
+```
+
+### `init` - Initialize a Curriculum
+
+Creates an initial trainer state for enrolling a subject in a curriculum. This generates the starting point for curriculum execution.
+
+**Required Arguments:**
+- `--curriculum <name>`: The curriculum to enroll in (required)
+
+**Optional Arguments:**
+- `--output <path>`: Path to save the enrollment trainer state as a JSON file
+- `--stage <name>`: If provided, enroll at a specific stage instead of the first stage
+
+**Examples:**
+
+Initialize the depletion curriculum (starts at first stage):
+
+```bash
+uv run curriculum init --curriculum coupled_baiting --output initial_state.json
+```
+
+Initialize at a specific stage:
+
+```bash
+uv run curriculum init --curriculum coupled_baiting --stage s_stage_1 --output initial_state.json
+```
+
+Print to stdout without saving:
+
+```bash
+uv run curriculum init --curriculum coupled_baiting
+```
+
+### `run` - Run a Curriculum
+
+Evaluates a curriculum based on session data and the current trainer state, producing a suggestion for the next stage.
+
+**Required Arguments:**
+- `--data-directory <path>`: Path to the session data directory for calculating metrics
+- `--input-trainer-state <path>`: Path to the current trainer state JSON file
+
+**Optional Arguments:**
+- `--curriculum <name>`: Forces the use of a specific curriculum, bypassing automatic detection
+- `--output-suggestion <path>`: Directory path to save the suggestion as `suggestion.json`
+- `--mute-suggestion`: Disables printing the suggestion to stdout (useful when only saving to file)
+
+**Examples:**
+
+Run curriculum with automatic detection:
+
+```bash
+uv run curriculum run \
+  --data-directory /path/to/session/data \
+  --input-trainer-state current_state.json \
+  --output-suggestion /path/to/output
+```
+
+Force a specific curriculum:
+
+```bash
+uv run curriculum run \
+  --data-directory /path/to/session/data \
+  --input-trainer-state current_state.json \
+  --curriculum depletion \
+  --output-suggestion /path/to/output
+```
+
+Run without saving (print to stdout only):
+
+```bash
+uv run curriculum run \
+  --data-directory /path/to/session/data \
+  --input-trainer-state current_state.json
+```
+
+Run and save without printing:
+
+```bash
+uv run curriculum run \
+  --data-directory /path/to/session/data \
+  --input-trainer-state current_state.json \
+  --output-suggestion /path/to/output \
+  --mute-suggestion
+```
+
+Quick demo with template curriculum:
+
+```bash
+uv run curriculum run \
+  --data-directory "demo" \
+  --input-trainer-state "foo.json" \
+  --curriculum "template"
+```
+
+### `version` - Show Package Version
+
+Displays the version of this package.
+
+**Usage:**
+
+```bash
+uv run curriculum version
+```
+
+**Example output:**
+```
+0.2.0
+```
+
+### `dsl-version` - Show DSL Version
+
+Displays the version of the underlying `aind-behavior-curriculum` DSL library.
+
+**Usage:**
+
+```bash
+uv run curriculum dsl-version
+```
+
+**Example output:**
+```
+0.0.37
+```
+
+## Typical Workflow
+
+1. **List available curricula:**
+   ```bash
+   uv run curriculum list
+   ```
+
+2. **Initialize a subject in a curriculum:**
+   ```bash
+   uv run curriculum init --curriculum depletion --output trainer_state.json
+   ```
+
+3. **After a training session, evaluate progress:**
+   ```bash
+   uv run curriculum run \
+     --data-directory /path/to/session/data \
+     --input-trainer-state trainer_state.json \
+     --output-suggestion /path/to/output
+   ```
+
+4. **Use the suggestion output for the next session:**
+   The `suggestion.json` file contains the updated trainer state and can be used as `--input-trainer-state` for the next session.
+
+
+## Style guide
+
+To keep things clear, I suggest the following naming convention:
+
+* **Policies** should start with `p_` (e.g., `p_identity_policy`)
+* **Policy transitions** should start with `pt_`
+* **Stages** should start with `s_` (e.g., `s_stage1`)
+* **Stage transitions** should start with `st_` and should be named after the stages they transition between (e.g., `st_s_stage1_s_stage2`)
+
+Define the following modules:
+
+* **metrics**: Defines (or imports) metrics classes and how to calculate them from data
+* **stages**: Defines the different stages of the Dynamic foraging task. This includes task settings and, optionally, policies
+* **curriculum**: Defines the transitions between the stages and generate entry point to the application
+
+## Contributors
+
+Contributions to this repository are welcome! However, please ensure that your code adheres to the recommended DevOps practices below:
+
+### Linting
+
+We use [ruff](https://docs.astral.sh/ruff/) as our primary linting tool.
+
+### Testing
+
+Attempt to add tests when new features are added.
+To run the currently available tests, run `uv run pytest` from the root of the repository.
+
+### Lock files
+
+We use [uv](https://docs.astral.sh/uv/) to manage our lock files and therefore encourage everyone to use uv as a package manager as well.
\ No newline at end of file
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/examples/coupled_baiting_curriculum.py b/workspace/aind_behavior_dynamic_foraging_curricula/examples/coupled_baiting_curriculum.py
new file mode 100644
index 0000000..9cf547b
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/examples/coupled_baiting_curriculum.py
@@ -0,0 +1,35 @@
+from pathlib import Path
+
+from aind_behavior_dynamic_foraging_curricula.coupled_baiting import TRAINER
+from aind_behavior_dynamic_foraging_curricula.metrics import DynamicForagingMetrics
+
+
+def main():
+    trainer_state = TRAINER.create_enrollment()
+
+    # starts at stage_1_warmup
+    current_stage = trainer_state.stage
+    print(f"Current stage: {current_stage.name}")  # stage_1_warmup
+
+    metrics = DynamicForagingMetrics(
+        total_sessions=1,
+        consecutive_sessions_at_current_stage=1,
+        unignored_trials_per_session=[250],
+        foraging_efficiency_per_session=[0.65],
+        stage_name="stage_1_warmup",
+    )
+
+    # evaluate
+    new_trainer_state = TRAINER.evaluate(trainer_state, metrics)
+    print(f"Next stage: {new_trainer_state.stage.name}")  # stage_2, since finished_trials >= 200 and efficiency >= 0.6
+
+    # save models
+    trainer_state_path = Path(r".\local\trainer_state.json")
+    trainer_state_path.write_text(new_trainer_state.model_dump_json(indent=2))
+
+    metrics_path = Path(r".\local\metrics.json")
+    metrics_path.write_text(metrics.model_dump_json(indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/pyproject.toml b/workspace/aind_behavior_dynamic_foraging_curricula/pyproject.toml
new file mode 100644
index 0000000..386167e
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/pyproject.toml
@@ -0,0 +1,75 @@
+[build-system]
+requires = ["uv_build>=0.8.22"]
+build-backend = "uv_build"
+
+[project]
+name = "aind-behavior-dynamic-foraging-curricula"
+description = "A library of curricula for the Dynamic Foraging task."
+authors = [
+    {name = "Bruno Cruz", email = "bruno.cruz@alleninstitute.org"},
+    {name = "Micah Woodard", email = "micah.woodard@alleninstitute.org"}
+    ]
+license = "MIT"
+version = "0.2.1"
+requires-python = ">=3.11"
+classifiers = [
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Operating System :: Microsoft :: Windows",
+]
+readme = {file = "README.md", content-type = "text/markdown"}
+
+dependencies = [
+    "aind-behavior-curriculum >= 0.0.38",
+    "numpy>=2.4.2",
+    "pydantic-settings",
+    "aind-behavior-dynamic-foraging==0.0.2rc24"
+]
+
+[tool.uv.sources]
+aind-behavior-dynamic-foraging = { workspace = true }
+
+[dependency-groups]
+
+dev = [
+    'ruff',
+    'pytest',
+    'pytest-cov',
+    'codespell',
+]
+
+docs = [
+    'mkdocs',
+    'mkdocs-material',
+    'mkdocstrings[python]',
+    'pymdown-extensions',
+    'ruff',
+]
+
+[tool.uv]
+default-groups = ['dev']
+
+[tool.ruff]
+line-length = 120
+target-version = 'py311'
+
+[tool.ruff.lint]
+extend-select = ['Q', 'RUF100', 'C90', 'I']
+extend-ignore = []
+mccabe = { max-complexity = 14 }
+pydocstyle = { convention = 'google' }
+
+[tool.codespell]
+skip = '.git,*.pdf,*.svg,uv.lock'
+ignore-words-list = 'nd'
+
+[tool.pytest.ini_options]
+addopts = "--strict-markers --tb=short --cov=src --cov-report=term-missing --cov-fail-under=70"
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+
+[project.scripts]
+curriculum = "aind_behavior_dynamic_foraging_curricula.cli:main"
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/__init__.py b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/__init__.py
new file mode 100644
index 0000000..98cffdf
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/__init__.py
@@ -0,0 +1,30 @@
+import re
+from importlib.metadata import PackageNotFoundError, version
+
+
+def pep440_to_semver(ver: str) -> str:
+    """
+    Convert a PEP 440 version to a SemVer-compatible string.
+
+    Examples:
+        1.2.3rc2   -> 1.2.3-rc2
+        1.2.3a1    -> 1.2.3-a1
+        1.2.3b1    -> 1.2.3-b1
+        1.2.3.dev4 -> 1.2.3-dev4
+        1.2.3.post1 -> 1.2.3+post1
+    """
+    # pre-release: a, b, rc -> -aN, -bN, -rcN
+    ver = re.sub(r"(?<=\d)(a|b|rc)(\d+)", r"-\1\2", ver)
+    # dev release: .devN -> -devN
+    ver = re.sub(r"\.dev(\d+)", r"-dev\1", ver)
+    # post release: .postN -> +postN
+    ver = re.sub(r"\.post(\d+)", r"+post\1", ver)
+    return ver
+
+
+try:
+    __version__ = version(__name__)
+except PackageNotFoundError:
+    __version__ = "0.0.0"
+
+__semver__ = pep440_to_semver(__version__)
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/cli.py b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/cli.py
new file mode 100644
index 0000000..62fa7b7
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/cli.py
@@ -0,0 +1,168 @@
+import importlib
+import logging
+import os
+import typing as t
+from pathlib import Path
+
+import aind_behavior_curriculum
+from pydantic import BaseModel, Field, RootModel, SerializeAsAny
+from pydantic_settings import BaseSettings, CliApp, CliImplicitFlag, CliSubCommand
+
+from . import __version__
+from .utils import model_from_json_file
+
+TModel = t.TypeVar("TModel", bound=BaseModel)
+TTrainerState = t.TypeVar("TTrainerState", bound=aind_behavior_curriculum.TrainerState)
+TMetrics = t.TypeVar("TMetrics", bound=aind_behavior_curriculum.Metrics)
+TCurriculum = t.TypeVar("TCurriculum", bound=aind_behavior_curriculum.Curriculum)
+
+curricula_logger = logging.Logger(__name__)
+
+
+class Version(RootModel):
+    root: t.Any
+
+    def cli_cmd(self) -> None:
+        print(__version__)
+
+
+class DslVersion(RootModel):
+    root: t.Any
+
+    def cli_cmd(self) -> None:
+        print(aind_behavior_curriculum.__version__)
+
+
+class ListKnownCurricula(RootModel):
+    root: t.Any
+
+    def cli_cmd(self) -> None:
+        print("Available curricula:")
+        for curriculum in _KNOWN_CURRICULA:
+            print(f" - {curriculum}")
+
+
+class CurriculumCliArgs(BaseSettings):
+    data_directory: os.PathLike = Field(description="Path to the session data directory.")
+    input_trainer_state: os.PathLike = Field(description="Path to a serialized trainer state.")
+    mute_suggestion: CliImplicitFlag[bool] = Field(default=False, description="Disables the suggestion output")
+    output_suggestion: t.Optional[os.PathLike] = Field(
+        default=None,
+        description="A path to save the suggestion. If not provided, the suggestion will not be serialized to a file.",
+    )
+    curriculum: t.Optional[str] = Field(
+        default=None, description="Forces the use of a specific curriculum, bypassing any automatic detection."
+    )
+
+    def cli_cmd(self) -> None:
+        try:
+            if self.curriculum:
+                curriculum_name = self.curriculum
+            else:
+                annonymous_trainer_state = model_from_json_file(
+                    self.input_trainer_state,
+                    aind_behavior_curriculum.TrainerState[aind_behavior_curriculum.Curriculum[t.Any]],
+                )
+                if (curriculum := annonymous_trainer_state.curriculum) is None:
+                    curricula_logger.error("Trainer state does not have a curriculum.")
+                    raise ValueError("Trainer state does not have a curriculum.")
+                curriculum_name = curriculum.pkg_location
+
+            curriculum_name = curriculum_name.replace(str(__package__) + ".", "")
+            if curriculum_name not in _KNOWN_CURRICULA:
+                curricula_logger.error(f"Unknown curriculum: {curriculum_name}. Available: {list(_KNOWN_CURRICULA)}")
+                raise ValueError(f"Unknown curriculum: {curriculum_name}. Available: {list(_KNOWN_CURRICULA)}")
+
+            else:
+                module = importlib.import_module(f"{__package__}.{curriculum_name}")
+                runner: t.Callable[[CurriculumCliArgs], CurriculumSuggestion] = getattr(module, "run_curriculum")
+
+            suggestion = runner(self)
+            suggestion.dsl_version = aind_behavior_curriculum.__version__
+
+            if not self.mute_suggestion:
+                print(suggestion.model_dump_json())
+
+            if self.output_suggestion is not None:
+                with open(Path(self.output_suggestion) / "suggestion.json", "w", encoding="utf-8") as file:
+                    file.write(suggestion.model_dump_json(indent=2))
+
+        except Exception as e:
+            curricula_logger.error(f"Error occurred while running curriculum: {e}")
+            raise e
+
+
+class CurriculumInitCliArgs(BaseSettings):
+    curriculum: str = Field(description="The curriculum to enroll the model in.")
+    output: t.Optional[os.PathLike] = Field(
+        default=None,
+        description="Path to save the enrollment curriculum. If not provided, the curriculum will not be serialized to a file.",
+    )
+    stage: t.Optional[str] = Field(
+        default=None,
+        description="If provided, the enrollment will be for a specific stage in the curriculum.",
+    )
+
+    def cli_cmd(self) -> None:
+        if self.curriculum not in _KNOWN_CURRICULA:
+            curricula_logger.error(f"Unknown curriculum: {self.curriculum}. Available: {list(_KNOWN_CURRICULA)}")
+            raise ValueError(f"Unknown curriculum: {self.curriculum}. Available: {list(_KNOWN_CURRICULA)}")
+
+        module = importlib.import_module(f"{__package__}.{self.curriculum}")
+        trainer: aind_behavior_curriculum.Trainer = getattr(module, "TRAINER")
+        if self.stage is None:
+            init_state = trainer.create_enrollment()
+        else:
+            try:
+                stages = trainer.curriculum.see_stages()
+                stage = [s for s in stages if s.name == self.stage][0]
+            except IndexError:
+                curricula_logger.error(f"Unknown stage: {self.stage}")
+                curricula_logger.error(self._print_available_stages(trainer.curriculum))
+                raise ValueError(f"Unknown stage: {self.stage}. Available: {[s.name for s in stages]}")
+            else:
+                init_state = trainer.create_trainer_state(
+                    stage=stage, is_on_curriculum=True, active_policies=stage.start_policies
+                )
+
+        if self.output is not None:
+            with open(Path(self.output), "w", encoding="utf-8") as file:
+                file.write(init_state.model_dump_json(indent=2))
+
+        print(init_state.model_dump_json())
+
+    def _print_available_stages(self, curriculum: aind_behavior_curriculum.Curriculum) -> None:
+        print("Available stages:")
+        for stage in curriculum.see_stages():
+            print(f" - {stage.name}")
+
+
+class CurriculumAppCliArgs(BaseSettings, cli_prog_name="curriculum", cli_kebab_case=True):
+    run: CliSubCommand[CurriculumCliArgs]
+    init: CliSubCommand[CurriculumInitCliArgs]
+    version: CliSubCommand[Version]
+    dsl_version: CliSubCommand[DslVersion]
+    list: CliSubCommand[ListKnownCurricula]
+
+    def cli_cmd(self) -> None:
+        CliApp.run_subcommand(self)
+
+
+class CurriculumSuggestion(BaseModel, t.Generic[TTrainerState, TMetrics]):
+    trainer_state: SerializeAsAny[TTrainerState] = Field(description="The TrainerState suggestion.")
+    metrics: SerializeAsAny[TMetrics] = Field(description="The calculated metrics.")
+    version: str = Field(default=__version__, description="The version of the curriculum.")
+    dsl_version: str = Field(
+        default=aind_behavior_curriculum.__version__, description="The version of the curriculum library."
+    )
+
+
+_KNOWN_CURRICULA = [p.stem for p in Path(__file__).parent.iterdir() if p.is_dir() and not p.name.startswith("_")]
+
+
+def main():
+    CliApp.run(CurriculumAppCliArgs)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/__init__.py b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/__init__.py
new file mode 100644
index 0000000..7e54700
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/__init__.py
@@ -0,0 +1,3 @@
+from .curriculum import CURRICULUM, CURRICULUM_NAME, PKG_LOCATION, TRAINER, run_curriculum
+
+__all__ = ["CURRICULUM_NAME", "CURRICULUM", "TRAINER", "PKG_LOCATION", "run_curriculum"]
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/curriculum.py b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/curriculum.py
new file mode 100644
index 0000000..f8db8b3
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/curriculum.py
@@ -0,0 +1,133 @@
+from typing import Any, Type, TypeVar
+
+import numpy as np
+import pydantic
+from aind_behavior_curriculum import Curriculum, Metrics, StageTransition, Trainer, TrainerState, create_curriculum
+from aind_behavior_dynamic_foraging.task_logic import (
+    AindDynamicForagingTaskLogic,
+)
+
+from .. import __semver__
+from ..cli import CurriculumCliArgs, CurriculumSuggestion
+from ..metrics import DynamicForagingMetrics
+from ..utils import metrics_from_dataset_path, trainer_state_from_file
+from .stages import (
+    make_s_stage_1,
+    make_s_stage_1_warmup,
+    make_s_stage_2,
+    make_s_stage_3,
+    make_s_stage_final,
+    make_s_stage_graduated,
+)
+
+CURRICULUM_NAME = "CoupledBaiting"
+PKG_LOCATION = ".".join(__name__.split(".")[:-1])
+
+TModel = TypeVar("TModel", bound=pydantic.BaseModel)
+
+
+# --- STAGE TRANSITIONS ---
+
+
+# warmup
+@StageTransition
+def st_stage_1_warmup_to_stage_1(metrics: DynamicForagingMetrics) -> bool:
+    return bool(metrics.consecutive_sessions_at_current_stage >= 1)
+
+
+@StageTransition
+def st_stage_1_warmup_to_stage_2(metrics: DynamicForagingMetrics) -> bool:
+    return bool(metrics.unignored_trials_per_session[-1] >= 200 and metrics.foraging_efficiency_per_session[-1] >= 0.6)
+
+
+# stage 1
+@StageTransition
+def st_stage_1_to_stage_2(metrics: DynamicForagingMetrics) -> bool:
+    return bool(metrics.foraging_efficiency_per_session[-1] >= 0.6 and metrics.unignored_trials_per_session[-1] >= 200)
+
+
+# stage 2
+@StageTransition
+def st_stage_2_to_stage_3(metrics: DynamicForagingMetrics) -> bool:
+    return bool(metrics.foraging_efficiency_per_session[-1] >= 0.65 and metrics.unignored_trials_per_session[-1] >= 300)
+
+
+@StageTransition
+def st_stage_2_to_stage_1(metrics: DynamicForagingMetrics) -> bool:
+    return bool(metrics.foraging_efficiency_per_session[-1] < 0.55 or metrics.unignored_trials_per_session[-1] < 200)
+
+
+# stage 3
+@StageTransition
+def st_stage_3_to_final(metrics: DynamicForagingMetrics) -> bool:
+    return bool(metrics.foraging_efficiency_per_session[-1] >= 0.7 and metrics.unignored_trials_per_session[-1] >= 400)
+
+
+@StageTransition
+def st_stage_3_to_stage_2(metrics: DynamicForagingMetrics) -> bool:
+    return bool(metrics.foraging_efficiency_per_session[-1] < 0.65 or metrics.unignored_trials_per_session[-1] < 300)
+
+
+# stage final
+@StageTransition
+def st_final_to_graduated(metrics: DynamicForagingMetrics) -> bool:
+    return bool(
+        metrics.total_sessions >= 10
+        and metrics.consecutive_sessions_at_current_stage >= 5
+        and np.mean(metrics.unignored_trials_per_session[-5:]) >= 450
+        and np.mean(metrics.foraging_efficiency_per_session[-5:]) >= 0.7
+    )
+
+
+@StageTransition
+def st_final_to_stage_3(metrics: DynamicForagingMetrics) -> bool:
+    return bool(
+        np.mean(metrics.foraging_efficiency_per_session[-5:]) < 0.60
+        or np.mean(metrics.unignored_trials_per_session[-5:]) < 300
+    )
+
+
+# --- CURRICULUM ---
+curriculum_class: Type[Curriculum[AindDynamicForagingTaskLogic]] = create_curriculum(
+    CURRICULUM_NAME, __semver__, (AindDynamicForagingTaskLogic,), pkg_location=PKG_LOCATION
+)
+CURRICULUM = curriculum_class()
+
+# add stages
+CURRICULUM.add_stage(make_s_stage_1_warmup())
+CURRICULUM.add_stage(make_s_stage_1())
+CURRICULUM.add_stage(make_s_stage_2())
+CURRICULUM.add_stage(make_s_stage_3())
+CURRICULUM.add_stage(make_s_stage_final())
+CURRICULUM.add_stage(make_s_stage_graduated())
+
+# add stage transitions
+# warmup
+CURRICULUM.add_stage_transition(
+    make_s_stage_1_warmup(), make_s_stage_2(), st_stage_1_warmup_to_stage_2
+)  # add 2 first to take priority
+
+CURRICULUM.add_stage_transition(make_s_stage_1_warmup(), make_s_stage_1(), st_stage_1_warmup_to_stage_1)
+# stage 1
+CURRICULUM.add_stage_transition(make_s_stage_1(), make_s_stage_2(), st_stage_1_to_stage_2)
+# stage 2
+CURRICULUM.add_stage_transition(make_s_stage_2(), make_s_stage_3(), st_stage_2_to_stage_3)
+CURRICULUM.add_stage_transition(make_s_stage_2(), make_s_stage_1(), st_stage_2_to_stage_1)
+# stage 3
+CURRICULUM.add_stage_transition(make_s_stage_3(), make_s_stage_final(), st_stage_3_to_final)
+CURRICULUM.add_stage_transition(make_s_stage_3(), make_s_stage_2(), st_stage_3_to_stage_2)
+# final
+CURRICULUM.add_stage_transition(make_s_stage_final(), make_s_stage_graduated(), st_final_to_graduated)
+CURRICULUM.add_stage_transition(make_s_stage_final(), make_s_stage_3(), st_final_to_stage_3)
+
+TRAINER = Trainer(CURRICULUM)
+
+
+def run_curriculum(args: CurriculumCliArgs) -> CurriculumSuggestion[TrainerState[Any], Any]:
+    trainer_state = trainer_state_from_file(args.input_trainer_state, TRAINER)
+    metrics: Metrics = metrics_from_dataset_path(
+        dataset_path=args.data_directory,
+        trainer_state=trainer_state,
+    )
+    trainer_state = TRAINER.evaluate(trainer_state, metrics)
+    return CurriculumSuggestion(trainer_state=trainer_state, metrics=metrics, version=__semver__)
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/stages.py b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/stages.py
new file mode 100644
index 0000000..66ee583
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/coupled_baiting/stages.py
@@ -0,0 +1,320 @@
+from aind_behavior_curriculum import MetricsProvider, Stage
+from aind_behavior_dynamic_foraging.task_logic import (
+    AindDynamicForagingTaskLogic,
+    AindDynamicForagingTaskParameters,
+    RewardSize,
+)
+from aind_behavior_dynamic_foraging.task_logic.trial_generators import (
+    CoupledTrialGeneratorSpec,
+    TrialGeneratorCompositeSpec,
+    WarmupTrialGeneratorSpec,
+)
+from aind_behavior_dynamic_foraging.task_logic.trial_generators.block_based_trial_generator import (
+    RewardProbabilityParameters,
+)
+from aind_behavior_dynamic_foraging.task_logic.trial_generators.coupled_trial_generator import (
+    BehaviorStabilityParameters,
+    CoupledTrialGenerationEndConditions,
+)
+from aind_behavior_dynamic_foraging.task_logic.trial_generators.warmup_trial_generator import (
+    WarmupTrialGenerationEndConditions,
+)
+from aind_behavior_services.task.distributions import (
+    ExponentialDistribution,
+    ExponentialDistributionParameters,
+    Scalar,
+    TruncationParameters,
+)
+
+from ..metrics import metrics_from_dataset
+
+# --- STAGES ---
+# adapted from https://github.com/AllenNeuralDynamics/aind-foraging-behavior-bonsai-automatic-training/blob/main/code/aind_auto_train/curriculums/coupled_baiting_2p3.py
+
+
+def make_s_stage_1_warmup():
+    return Stage(
+        name="stage_1_warmup",
+        task=AindDynamicForagingTaskLogic(
+            task_parameters=AindDynamicForagingTaskParameters(
+                reward_size=RewardSize(right_value_volume=4.0, left_value_volume=4.0),
+                trial_generator=TrialGeneratorCompositeSpec(
+                    generators=[
+                        WarmupTrialGeneratorSpec(
+                            trial_generation_end_parameters=WarmupTrialGenerationEndConditions(
+                                min_trial=50,
+                                max_choice_bias=0.1,
+                                min_response_rate=0.8,
+                                evaluation_window=20,
+                            ),
+                            reward_probability_parameters=RewardProbabilityParameters(
+                                base_reward_sum=1, reward_pairs=[[1.0, 0.0]]
+                            ),
+                            block_len=Scalar(value=1),
+                            inter_trial_interval_duration=ExponentialDistribution(
+                                distribution_parameters=ExponentialDistributionParameters(rate=1.0 / 3),
+                                truncation_parameters=TruncationParameters(min=1, max=7),
+                            ),
+                            quiescent_duration=Scalar(value=0.1),
+                            min_block_reward=1,
+                            is_baiting=True,
+                            response_duration=5.0,
+                            reward_consumption_duration=1.0,
+                            kernel_size=2,
+                            extend_block_on_no_response=True,
+                        ),
+                        CoupledTrialGeneratorSpec(
+                            trial_generation_end_parameters=CoupledTrialGenerationEndConditions(
+                                max_trial=1000,
+                                max_time=75,
+                                min_time=30,
+                                ignore_win=20000,
+                                ignore_ratio_threshold=1,
+                            ),
+                            behavior_stability_parameters=BehaviorStabilityParameters(
+                                behavior_evaluation_mode="end",
+                                behavior_stability_fraction=0.5,
+                                min_consecutive_stable_trials=5,
+                            ),
+                            reward_probability_parameters=RewardProbabilityParameters(
+                                base_reward_sum=0.8, reward_pairs=[[1.0, 0.0]]
+                            ),
+                            block_len=ExponentialDistribution(
+                                distribution_parameters=ExponentialDistributionParameters(rate=0.2),
+                                truncation_parameters=TruncationParameters(min=10, max=20),
+                            ),
+                            inter_trial_interval_duration=ExponentialDistribution(
+                                distribution_parameters=ExponentialDistributionParameters(rate=1.0 / 3),
+                                truncation_parameters=TruncationParameters(min=1, max=7),
+                            ),
+                            quiescent_duration=Scalar(value=0.1),
+                            min_block_reward=0,
+                            is_baiting=True,
+                            extend_block_on_no_response=True,
+                            response_duration=5.0,
+                            reward_consumption_duration=1.0,
+                            kernel_size=2,
+                        ),
+                    ]
+                ),
+            )
+        ),
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
+
+
+def make_s_stage_1():
+    return Stage(
+        name="stage_1",
+        task=AindDynamicForagingTaskLogic(
+            task_parameters=AindDynamicForagingTaskParameters(
+                reward_size=RewardSize(right_value_volume=2.0, left_value_volume=2.0),
+                lick_spout_retraction=False,
+                trial_generator=CoupledTrialGeneratorSpec(
+                    trial_generation_end_parameters=CoupledTrialGenerationEndConditions(
+                        max_trial=1000,
+                        max_time=75,
+                        min_time=30,
+                        ignore_win=20000,
+                        ignore_ratio_threshold=1,
+                    ),
+                    behavior_stability_parameters=BehaviorStabilityParameters(
+                        behavior_evaluation_mode="end",
+                        behavior_stability_fraction=0.5,
+                        min_consecutive_stable_trials=5,
+                    ),
+                    reward_probability_parameters=RewardProbabilityParameters(
+                        base_reward_sum=0.8, reward_pairs=[[1.0, 0.0]]
+                    ),
+                    block_len=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=0.2),
+                        truncation_parameters=TruncationParameters(min=10, max=20),
+                    ),
+                    inter_trial_interval_duration=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=1.0 / 3),
+                        truncation_parameters=TruncationParameters(min=1, max=7),
+                    ),
+                    quiescent_duration=Scalar(value=0.1),
+                    min_block_reward=0,
+                    is_baiting=False,
+                    extend_block_on_no_response=True,
+                    response_duration=5.0,
+                    reward_consumption_duration=1.0,
+                    kernel_size=2,
+                ),
+            )
+        ),
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
+
+
+def make_s_stage_2():
+    return Stage(
+        name="stage_2",
+        task=AindDynamicForagingTaskLogic(
+            task_parameters=AindDynamicForagingTaskParameters(
+                reward_size=RewardSize(right_value_volume=2.0, left_value_volume=2.0),
+                lick_spout_retraction=False,
+                trial_generator=CoupledTrialGeneratorSpec(
+                    trial_generation_end_parameters=CoupledTrialGenerationEndConditions(
+                        max_trial=1000,
+                        max_time=75,
+                        min_time=30,
+                        ignore_win=30,
+                        ignore_ratio_threshold=0.83,
+                    ),
+                    behavior_stability_parameters=BehaviorStabilityParameters(
+                        behavior_evaluation_mode="end",
+                        behavior_stability_fraction=0.6,
+                        min_consecutive_stable_trials=5,
+                    ),
+                    reward_probability_parameters=RewardProbabilityParameters(
+                        base_reward_sum=0.6, reward_pairs=[[8, 1]]
+                    ),
+                    block_len=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=0.1),
+                        truncation_parameters=TruncationParameters(min=10, max=40),
+                    ),
+                    inter_trial_interval_duration=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=0.2),
+                        truncation_parameters=TruncationParameters(min=1, max=10),
+                    ),
+                    quiescent_duration=Scalar(value=0.3),
+                    min_block_reward=0,
+                    is_baiting=True,
+                    extend_block_on_no_response=True,
+                    response_duration=3.0,
+                    reward_consumption_duration=1.0,
+                    kernel_size=2,
+                ),
+            )
+        ),
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
+
+
+def make_s_stage_3():
+    return Stage(
+        name="stage_3",
+        task=AindDynamicForagingTaskLogic(
+            task_parameters=AindDynamicForagingTaskParameters(
+                reward_size=RewardSize(right_value_volume=2.0, left_value_volume=2.0),
+                lick_spout_retraction=False,
+                trial_generator=CoupledTrialGeneratorSpec(
+                    trial_generation_end_parameters=CoupledTrialGenerationEndConditions(
+                        max_trial=1000,
+                        max_time=75,
+                        min_time=30,
+                        ignore_win=30,
+                        ignore_ratio_threshold=0.83,
+                    ),
+                    behavior_stability_parameters=BehaviorStabilityParameters(
+                        behavior_evaluation_mode="end",
+                        behavior_stability_fraction=0.6,
+                        min_consecutive_stable_trials=5,
+                    ),
+                    reward_probability_parameters=RewardProbabilityParameters(
+                        base_reward_sum=0.45, reward_pairs=[[8, 1]]
+                    ),
+                    block_len=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=0.05),
+                        truncation_parameters=TruncationParameters(min=20, max=60),
+                    ),
+                    inter_trial_interval_duration=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=1.0 / 3),
+                        truncation_parameters=TruncationParameters(min=1, max=15),
+                    ),
+                    quiescent_duration=Scalar(value=0.5),
+                    min_block_reward=0,
+                    is_baiting=True,
+                    extend_block_on_no_response=True,
+                    response_duration=2.0,
+                    reward_consumption_duration=1.0,
+                    kernel_size=2,
+                ),
+            )
+        ),
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
+
+
+def make_s_stage_final():
+    return Stage(
+        name="final",
+        task=AindDynamicForagingTaskLogic(
+            task_parameters=AindDynamicForagingTaskParameters(
+                reward_size=RewardSize(right_value_volume=2.0, left_value_volume=2.0),
+                lick_spout_retraction=False,
+                trial_generator=CoupledTrialGeneratorSpec(
+                    trial_generation_end_parameters=CoupledTrialGenerationEndConditions(
+                        max_trial=1000,
+                        max_time=75,
+                        min_time=30,
+                        ignore_win=30,
+                        ignore_ratio_threshold=0.83,
+                    ),
+                    behavior_stability_parameters=None,
+                    reward_probability_parameters=RewardProbabilityParameters(
+                        base_reward_sum=0.45, reward_pairs=[[8, 1], [6, 1], [3, 1], [1, 1]]
+                    ),
+                    block_len=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=0.05),
+                        truncation_parameters=TruncationParameters(min=20, max=60),
+                    ),
+                    inter_trial_interval_duration=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=1.0 / 3),
+                        truncation_parameters=TruncationParameters(min=1, max=30),
+                    ),
+                    quiescent_duration=Scalar(value=1),
+                    min_block_reward=0,
+                    is_baiting=True,
+                    extend_block_on_no_response=True,
+                    response_duration=1.0,
+                    reward_consumption_duration=3.0,
+                    kernel_size=2,
+                ),
+            )
+        ),
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
+
+
+def make_s_stage_graduated():
+    return Stage(
+        name="graduated",
+        task=AindDynamicForagingTaskLogic(
+            task_parameters=AindDynamicForagingTaskParameters(
+                reward_size=RewardSize(right_value_volume=2.0, left_value_volume=2.0),
+                lick_spout_retraction=False,
+                trial_generator=CoupledTrialGeneratorSpec(
+                    trial_generation_end_parameters=CoupledTrialGenerationEndConditions(
+                        max_trial=1000,
+                        max_time=75,
+                        min_time=30,
+                        ignore_win=30,
+                        ignore_ratio_threshold=0.83,
+                    ),
+                    behavior_stability_parameters=None,
+                    reward_probability_parameters=RewardProbabilityParameters(
+                        base_reward_sum=0.45, reward_pairs=[[8, 1], [6, 1], [3, 1], [1, 1]]
+                    ),
+                    block_len=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=0.05),
+                        truncation_parameters=TruncationParameters(min=20, max=60),
+                    ),
+                    inter_trial_interval_duration=ExponentialDistribution(
+                        distribution_parameters=ExponentialDistributionParameters(rate=1.0 / 3),
+                        truncation_parameters=TruncationParameters(min=1, max=30),
+                    ),
+                    quiescent_duration=Scalar(value=1),
+                    min_block_reward=0,
+                    is_baiting=True,
+                    extend_block_on_no_response=True,
+                    response_duration=1.0,
+                    reward_consumption_duration=3.0,
+                    kernel_size=2,
+                ),
+            )
+        ),
+        metrics_provider=MetricsProvider(metrics_from_dataset),
+    )
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/metrics.py b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/metrics.py
new file mode 100644
index 0000000..76f0922
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/metrics.py
@@ -0,0 +1,158 @@
+import logging
+import os
+from typing import List, Literal
+
+import numpy as np
+from aind_behavior_curriculum import Metrics
+from aind_behavior_dynamic_foraging.data_contract import dataset as df_foraging_dataset
+from pydantic import Field
+
+STAGE_NAMES = Literal["stage_1_warmup", "stage_1", "stage_2", "stage_3", "final", "graduated"]
+
+logger = logging.getLogger(__name__)
+
+
+class DynamicForagingMetrics(Metrics):
+    """Metrics for dynamic foraging"""
+
+    foraging_efficiency_per_session: List[float] = Field(
+        min_length=1, description="Full history of foraging efficiency per session"
+    )
+    unignored_trials_per_session: List[int] = Field(
+        min_length=1, description="Full history of trials finished per session"
+    )
+    total_sessions: int = Field(ge=0, description="Total sessions completed.")
+    consecutive_sessions_at_current_stage: int = Field(ge=0, description="Last consecutive sessions at current stage.")
+    stage_name: STAGE_NAMES = Field(description="Stage name of session.")
+
+
+def metrics_from_dataset(
+    data_directory: os.PathLike,
+) -> DynamicForagingMetrics:
+    """
+    Create metrics for completed session.
+
+    Args:
+        data_directory (os.PathLike):
+            Path to the directory containing the dataset to analyze. This
+            directory is expected to include all required behavioral data files. Also includes metrics and trainer state
+
+    Returns:
+        DynamicForagingMetrics:
+            Metrics for session
+
+    Raises:
+        FileNotFoundError:
+            If the specified data directory or required files do not exist.
+
+        ValueError:
+            If the dataset is malformed or missing required fields for
+            computing metrics.
+    """
+
+    dataset = df_foraging_dataset(data_directory)
+    software_events = dataset["Behavior"]["SoftwareEvents"]
+    software_events.load_all()
+
+    trial_generator_spec = software_events["TrialGeneratorSpec"].data["data"].iloc[-1]
+    is_baiting = trial_generator_spec.get("trial_generator_spec", False)
+    trial_outcomes = software_events["TrialOutcome"].data["data"].iloc
+    # exclude auto response and ignored trials
+    filtered = [
+        t for t in trial_outcomes if t["is_right_choice"] is not None and not t["trial"]["is_auto_response_right"]
+    ]
+    is_right_choice = [to["is_right_choice"] for to in filtered]
+    is_rewarded = [to["is_rewarded"] for to in filtered]
+    p_right_reward = [to["trial"]["p_reward_right"] for to in filtered]
+    p_left_reward = [to["trial"]["p_reward_left"] for to in filtered]
+    foraging_efficiency = compute_foraging_efficiency(
+        is_baiting=is_baiting, is_rewarded=is_rewarded, p_left_reward=p_left_reward, p_right_reward=p_right_reward
+    )
+    logger.debug(f"Calculated foraging efficiency as {foraging_efficiency}")
+
+    try:
+        prev_metrics = DynamicForagingMetrics(**dataset["Behavior"]["PreviousMetrics"].data)
+        prev_stage = prev_metrics.stage_name
+    except FileNotFoundError:
+        logger.info("No previous metrics found.")
+        prev_metrics = None
+        prev_stage = None
+
+    foraging_efficiency_per_session = [] if not prev_metrics else prev_metrics.foraging_efficiency_per_session
+    unignored_trials_per_session = [] if not prev_metrics else prev_metrics.unignored_trials_per_session
+    total_sessions = 0 if not prev_metrics else prev_metrics.total_sessions
+    stage_name = dataset["Behavior"]["TrainerState"].data.stage.name
+    consecutive_sessions_at_current_stage = (
+        0 if not prev_metrics or stage_name != prev_stage else prev_metrics.consecutive_sessions_at_current_stage
+    )
+
+    return DynamicForagingMetrics(
+        foraging_efficiency_per_session=foraging_efficiency_per_session + [foraging_efficiency],
+        unignored_trials_per_session=unignored_trials_per_session + [sum(x is not None for x in is_right_choice)],
+        total_sessions=total_sessions + 1,
+        consecutive_sessions_at_current_stage=consecutive_sessions_at_current_stage + 1,
+        stage_name=stage_name,
+    )
+
+
+def compute_foraging_efficiency(
+    is_baiting: bool, is_rewarded: list[bool], p_right_reward: list[float], p_left_reward: list[float]
+) -> float:
+    """
+    Compute foraging efficiency for a two-arm bandit task.
+
+    This function calculates the ratio of actual rewards obtained to the
+    optimal expected rewards for a session. The implementation is adapted from the Allen Institute dynamic foraging
+    analysis codebase.
+
+    Args:
+        is_baiting (bool):
+            Whether the task uses a baiting schedule. If True, rewards can
+            accumulate on unchosen options; if False, rewards are independent
+            per trial.
+
+        is_rewarded (list[bool | None]):
+            List indicating whether each trial resulted in a reward. `True`
+            indicates a rewarded trial, `False` indicates no reward.
+
+        p_right_reward (list[float]):
+            Probability of reward for the right option on each trial.
+
+        p_left_reward (list[float]):
+            Probability of reward for the left option on each trial.
+
+    Returns:
+        float:
+            Foraging efficiency, defined as the ratio of the number of
+            rewarded trials to the optimal expected number of rewards for
+            the session.
+
+    Raises:
+        ValueError:
+            If input lists have mismatched lengths.
+
+    Notes:
+        Adapted from:
+        https://github.com/AllenNeuralDynamics/aind-dynamic-foraging-basic-analysis/blob/main/src/aind_dynamic_foraging_basic_analysis/metrics/foraging_efficiency.py
+    """
+
+    if not is_baiting:
+        logger.debug("Calculated non baiting foraging efficiency.")
+        optimal_rewards_per_session = np.nanmean(np.max([p_right_reward], axis=0)) * len(p_left_reward)
+    else:
+        logger.debug("Calculated baiting foraging efficiency.")
+        p_max = np.maximum(p_left_reward, p_right_reward)
+        p_min = np.minimum(p_left_reward, p_right_reward)
+
+        with np.errstate(divide="ignore", invalid="ignore"):
+            optimal_visit_ratio = np.floor(np.log(1 - p_max) / np.log(1 - p_min))
+            optimal_general_reward_rates = p_max + (1 - (1 - p_min) ** (optimal_visit_ratio + 1) - p_max**2) / (
+                optimal_visit_ratio + 1
+            )
+
+        simple_case = (p_min == 0) | (p_max >= 1)
+        optimal_reward_per_trial = np.where(simple_case, p_max, optimal_general_reward_rates)
+
+        optimal_rewards_per_session = np.nanmean(optimal_reward_per_trial) * len(p_left_reward)
+
+    return float(is_rewarded.count(True) / optimal_rewards_per_session)
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/utils.py b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/utils.py
new file mode 100644
index 0000000..a6fa4d9
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/src/aind_behavior_dynamic_foraging_curricula/utils.py
@@ -0,0 +1,31 @@
+import os
+from pathlib import Path
+from typing import Any, TypeVar
+
+import pydantic
+from aind_behavior_curriculum import Curriculum, Metrics, Trainer, TrainerState
+
+TModel = TypeVar("TModel", bound=pydantic.BaseModel)
+TCurriculum = TypeVar("TCurriculum", bound=Curriculum)
+
+
+def model_from_json_file(json_path: os.PathLike | str, model: type[TModel]) -> TModel:
+    with open(Path(json_path), "r", encoding="utf-8") as file:
+        return model.model_validate_json(file.read())
+
+
+def trainer_state_from_file(path: str | os.PathLike, trainer: Trainer[TCurriculum]) -> TrainerState[TCurriculum]:
+    return model_from_json_file(path, trainer.trainer_state_model)
+
+
+def metrics_from_dataset_path(
+    dataset_path: str | os.PathLike,
+    trainer_state: TrainerState[Any],
+) -> Metrics:
+    stage = trainer_state.stage
+    if stage is None:
+        raise ValueError("Trainer state does not have a stage")
+    if stage.metrics_provider is None:
+        raise ValueError("Stage does not have a metrics provider")
+    metrics_provider = stage.metrics_provider
+    return metrics_provider.callable(dataset_path)
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/tests/test_coupled_baiting.py b/workspace/aind_behavior_dynamic_foraging_curricula/tests/test_coupled_baiting.py
new file mode 100644
index 0000000..2a80908
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/tests/test_coupled_baiting.py
@@ -0,0 +1,205 @@
+import unittest
+
+from aind_behavior_dynamic_foraging_curricula.coupled_baiting import CURRICULUM, TRAINER
+from aind_behavior_dynamic_foraging_curricula.coupled_baiting.stages import (
+    make_s_stage_1,
+    make_s_stage_1_warmup,
+    make_s_stage_2,
+    make_s_stage_3,
+    make_s_stage_final,
+    make_s_stage_graduated,
+)
+from aind_behavior_dynamic_foraging_curricula.metrics import DynamicForagingMetrics
+
+
+def make_metrics(
+    foraging_efficiency_per_session: list[float] = None,
+    unignored_trials_per_session: list[int] = None,
+    total_sessions: int = 1,
+    consecutive_sessions_at_current_stage: int = 1,
+    stage_name: str = "stage_1_warmup",
+) -> DynamicForagingMetrics:
+    return DynamicForagingMetrics(
+        foraging_efficiency_per_session=foraging_efficiency_per_session or [0.0],
+        unignored_trials_per_session=unignored_trials_per_session or [0],
+        total_sessions=total_sessions,
+        consecutive_sessions_at_current_stage=consecutive_sessions_at_current_stage,
+        stage_name=stage_name,
+    )
+
+
+class TestCurriculumStructure(unittest.TestCase):
+    def test_all_stages_in_curriculum(self):
+        stages = CURRICULUM.see_stages()
+        stage_names = [s.name for s in stages]
+        self.assertIn("stage_1_warmup", stage_names)
+        self.assertIn("stage_1", stage_names)
+        self.assertIn("stage_2", stage_names)
+        self.assertIn("stage_3", stage_names)
+        self.assertIn("final", stage_names)
+        self.assertIn("graduated", stage_names)
+
+    def test_enrollment_starts_at_stage_1_warmup(self):
+        trainer_state = TRAINER.create_enrollment()
+        self.assertEqual(trainer_state.stage.name, "stage_1_warmup")
+
+
+class TestWarmupTransitions(unittest.TestCase):
+    def setUp(self):
+        self.trainer_state = TRAINER.create_trainer_state(stage=make_s_stage_1_warmup())
+
+    def test_warmup_to_stage_2_on_good_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[250], foraging_efficiency_per_session=[0.65], stage_name="stage_1_warmup"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_2")
+
+    def test_warmup_to_stage_1_after_first_session(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[100],
+            foraging_efficiency_per_session=[0.4],
+            consecutive_sessions_at_current_stage=1,
+            stage_name="stage_1_warmup",
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_1")
+
+
+class TestStage1Transitions(unittest.TestCase):
+    def setUp(self):
+        self.trainer_state = TRAINER.create_trainer_state(stage=make_s_stage_1())
+
+    def test_stage_1_to_stage_2_on_good_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[200], foraging_efficiency_per_session=[0.6], stage_name="stage_1"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_2")
+
+    def test_stage_1_no_transition_on_poor_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[100], foraging_efficiency_per_session=[0.4], stage_name="stage_1"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_1")
+
+
+class TestStage2Transitions(unittest.TestCase):
+    def setUp(self):
+        self.trainer_state = TRAINER.create_trainer_state(stage=make_s_stage_2())
+
+    def test_stage_2_to_stage_3_on_good_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[300], foraging_efficiency_per_session=[0.65], stage_name="stage_2"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_3")
+
+    def test_stage_2_rollback_to_stage_1_on_poor_trials(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[150], foraging_efficiency_per_session=[0.6], stage_name="stage_2"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_1")
+
+    def test_stage_2_rollback_to_stage_1_on_poor_efficiency(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[199], foraging_efficiency_per_session=[0.5], stage_name="stage_2"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_1")
+
+    def test_stage_2_no_transition_on_middle_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[250], foraging_efficiency_per_session=[0.6], stage_name="stage_2"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_2")
+
+
+class TestStage3Transitions(unittest.TestCase):
+    def setUp(self):
+        self.trainer_state = TRAINER.create_trainer_state(stage=make_s_stage_3())
+
+    def test_stage_3_to_final_on_good_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[400], foraging_efficiency_per_session=[0.7], stage_name="stage_3"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "final")
+
+    def test_stage_3_rollback_to_stage_2_on_poor_trials(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[250], foraging_efficiency_per_session=[0.7], stage_name="stage_3"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_2")
+
+    def test_stage_3_rollback_to_stage_2_on_poor_efficiency(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[299], foraging_efficiency_per_session=[0.6], stage_name="stage_3"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_2")
+
+    def test_stage_3_no_transition_on_middle_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[350], foraging_efficiency_per_session=[0.67], stage_name="stage_3"
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_3")
+
+
+class TestFinalTransitions(unittest.TestCase):
+    def setUp(self):
+        self.trainer_state = TRAINER.create_trainer_state(stage=make_s_stage_final())
+
+    def test_final_to_graduated_on_excellent_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[450] * 5,
+            foraging_efficiency_per_session=[0.70] * 5,
+            total_sessions=10,
+            consecutive_sessions_at_current_stage=5,
+            stage_name="final",
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "graduated")
+
+    def test_final_rollback_to_stage_3_on_poor_performance(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[250] * 5,
+            foraging_efficiency_per_session=[0.55] * 5,
+            total_sessions=10,
+            consecutive_sessions_at_current_stage=5,
+            stage_name="final",
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "stage_3")
+
+    def test_final_no_graduation_without_enough_sessions(self):
+        metrics = make_metrics(
+            unignored_trials_per_session=[450] * 5,
+            foraging_efficiency_per_session=[0.70] * 5,
+            total_sessions=5,
+            consecutive_sessions_at_current_stage=3,
+            stage_name="final",
+        )
+        updated = TRAINER.evaluate(self.trainer_state, metrics)
+        self.assertNotEqual(updated.stage.name, "graduated")
+
+    def test_graduated_is_absorbing(self):
+        trainer_state = TRAINER.create_trainer_state(stage=make_s_stage_graduated())
+        metrics = make_metrics(
+            unignored_trials_per_session=[500] * 5,
+            foraging_efficiency_per_session=[0.9] * 5,
+            total_sessions=20,
+            consecutive_sessions_at_current_stage=10,
+            stage_name="final",
+        )
+        updated = TRAINER.evaluate(trainer_state, metrics)
+        self.assertEqual(updated.stage.name, "graduated")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/workspace/aind_behavior_dynamic_foraging_curricula/tests/test_metrics.py b/workspace/aind_behavior_dynamic_foraging_curricula/tests/test_metrics.py
new file mode 100644
index 0000000..43933fd
--- /dev/null
+++ b/workspace/aind_behavior_dynamic_foraging_curricula/tests/test_metrics.py
@@ -0,0 +1,127 @@
+import unittest
+from pathlib import Path
+from typing import Optional
+from unittest.mock import MagicMock, PropertyMock, patch
+
+import numpy as np
+
+from aind_behavior_dynamic_foraging_curricula.metrics import (
+    metrics_from_dataset,
+)
+
+
+def _make_trial(
+    is_right_choice: Optional[bool],
+    is_rewarded: bool,
+    p_reward_right: float,
+    p_reward_left: float,
+    is_auto_response_right: Optional[bool] = False,
+) -> dict:
+    return {
+        "is_right_choice": is_right_choice,
+        "is_rewarded": is_rewarded,
+        "trial": {
+            "p_reward_right": p_reward_right,
+            "p_reward_left": p_reward_left,
+            "is_auto_response_right": is_auto_response_right,
+        },
+    }
+
+
+def _patch_dataset(
+    trials: list[dict], is_baiting: bool = True, prev_metrics: Optional[dict] = None, stage_name: str = "stage_1"
+):
+    """Patch df_foraging_dataset with a mock matching the access pattern in metrics_from_dataset."""
+
+    # software events
+    mock_trial_spec = MagicMock()
+    mock_trial_spec.data = {"data": MagicMock()}
+    mock_trial_spec.data["data"].iloc.__getitem__ = MagicMock(return_value={"is_baiting": is_baiting})
+    mock_trial_spec.data["data"].iloc[-1] = {"is_baiting": is_baiting}
+
+    # trial outcomes
+    mock_trial_outcome = MagicMock()
+    mock_trial_outcome.data = {"data": MagicMock(iloc=trials)}
+
+    # trial generator
+    mock_software_events = MagicMock()
+    mock_software_events.__getitem__ = MagicMock(
+        side_effect=lambda key: mock_trial_spec if key == "TrialGeneratorSpec" else mock_trial_outcome
+    )
+
+    # trainer state
+    mock_trainer_state = MagicMock()
+    mock_trainer_state.data = {"stage": {"name": stage_name}}
+
+    # previous metrics
+    mock_previous_metrics = MagicMock()
+    if prev_metrics is None:
+        type(mock_previous_metrics).data = PropertyMock(side_effect=FileNotFoundError)
+    else:
+        mock_previous_metrics.data = prev_metrics
+
+    mock_behavior = MagicMock()
+    mock_behavior.__getitem__ = MagicMock(
+        side_effect=lambda key: {
+            "SoftwareEvents": mock_software_events,
+            "TrainerState": mock_trainer_state,
+            "PreviousMetrics": mock_previous_metrics,
+        }[key]
+    )
+
+    mock_dataset = MagicMock()
+    mock_dataset.__getitem__ = MagicMock(return_value=mock_behavior)
+    return patch(
+        "aind_behavior_dynamic_foraging_curricula.metrics.df_foraging_dataset",
+        return_value=mock_dataset,
+    )
+
+
+class TestMetricsFromDataset(unittest.TestCase):
+    def setUp(self):
+        import tempfile
+
+        self.tmp_dir = tempfile.mkdtemp()
+        self.tmp_path = Path(self.tmp_dir)
+
+    def test_no_previous_metrics_total_sessions_is_one(self):
+        trials = [_make_trial(True, True, 0.7, 0.3)]
+        with _patch_dataset(trials):
+            result = metrics_from_dataset(self.tmp_path)
+        self.assertEqual(result.total_sessions, 1)
+
+    def test_no_previous_metrics_consecutive_sessions_is_one(self):
+        trials = [_make_trial(True, True, 0.7, 0.3)]
+        with _patch_dataset(trials):
+            result = metrics_from_dataset(self.tmp_path)
+        self.assertEqual(result.consecutive_sessions_at_current_stage, 1)
+
+    def test_previous_metrics_accumulate(self):
+        trials = [_make_trial(True, True, 0.7, 0.3)]
+        metrics = {
+            "foraging_efficiency_per_session": [0.5],
+            "unignored_trials_per_session": [10],
+            "total_sessions": 1,
+            "consecutive_sessions_at_current_stage": 1,
+            "stage_name": "stage_1_warmup",
+        }
+        with _patch_dataset(trials, prev_metrics=metrics):
+            result = metrics_from_dataset(self.tmp_path)
+        self.assertEqual(result.total_sessions, 2)
+        self.assertEqual(len(result.foraging_efficiency_per_session), 2)
+        self.assertEqual(len(result.unignored_trials_per_session), 2)
+
+    def test_foraging_efficiency_is_finite_and_positive(self):
+        trials = [
+            _make_trial(True, True, 0.7, 0.3),
+            _make_trial(True, False, 0.7, 0.3),
+            _make_trial(False, True, 0.7, 0.3),
+        ]
+        with _patch_dataset(trials):
+            result = metrics_from_dataset(self.tmp_path)
+        self.assertGreater(result.foraging_efficiency_per_session[-1], 0)
+        self.assertTrue(np.isfinite(result.foraging_efficiency_per_session[-1]))
+
+
+if __name__ == "__main__":
+    unittest.main()