diff --git a/docs/source/actuators.rst b/docs/source/actuators.rst
index 400e17079..eebcb2371 100644
--- a/docs/source/actuators.rst
+++ b/docs/source/actuators.rst
@@ -541,7 +541,7 @@ Domain Randomization
 .. code-block:: python
 
     from mjlab.envs.mdp import events
-    from mjlab.managers.manager_term_config import EventTermCfg
+    from mjlab.managers.event_manager import EventTermCfg
     from mjlab.managers.scene_entity_config import SceneEntityCfg
 
     EventTermCfg(
diff --git a/docs/source/faq.rst b/docs/source/faq.rst
index dc448e1a9..483d8a36e 100644
--- a/docs/source/faq.rst
+++ b/docs/source/faq.rst
@@ -81,7 +81,7 @@ My training crashes with NaN errors
       from dataclasses import dataclass, field
 
       from mjlab.envs.mdp.terminations import nan_detection
-      from mjlab.managers.manager_term_config import TerminationTermCfg
+      from mjlab.managers.termination_manager import TerminationTermCfg
 
       @dataclass
       class TerminationCfg:
diff --git a/docs/source/nan_guard.rst b/docs/source/nan_guard.rst
index cbc7cb465..2cacee24c 100644
--- a/docs/source/nan_guard.rst
+++ b/docs/source/nan_guard.rst
@@ -119,7 +119,7 @@ While ``nan_guard`` helps **debug** NaN issues by capturing states, you can also
 .. code-block:: python
 
     from mjlab.envs.mdp.terminations import nan_detection
-    from mjlab.managers.manager_term_config import TerminationTermCfg
+    from mjlab.managers.termination_manager import TerminationTermCfg
 
     # In your termination config:
     nan_term: TerminationTermCfg = field(
diff --git a/docs/source/observation.rst b/docs/source/observation.rst
index 759e10cec..238d3fefc 100644
--- a/docs/source/observation.rst
+++ b/docs/source/observation.rst
@@ -13,7 +13,7 @@ TL;DR
 
 .. code-block:: python
 
-    from mjlab.managers.manager_term_config import ObservationTermCfg
+    from mjlab.managers.observation_manager import ObservationTermCfg
 
     joint_vel: ObservationTermCfg = ObservationTermCfg(
         func=joint_vel,
diff --git a/docs/source/randomization.rst b/docs/source/randomization.rst
index d1983dd0a..25ccb987e 100644
--- a/docs/source/randomization.rst
+++ b/docs/source/randomization.rst
@@ -15,7 +15,7 @@ apply the draw.
 
 .. code-block:: python
 
-    from mjlab.managers.manager_term_config import EventTermCfg
+    from mjlab.managers.event_manager import EventTermCfg
     from mjlab.managers.scene_entity_config import SceneEntityCfg
     from mjlab.envs import mdp
 
diff --git a/scripts/benchmarks/measure_throughput.py b/scripts/benchmarks/measure_throughput.py
index 17504a05d..73f7ace8b 100644
--- a/scripts/benchmarks/measure_throughput.py
+++ b/scripts/benchmarks/measure_throughput.py
@@ -126,7 +126,7 @@ def benchmark_task(task: str, cfg: ThroughputConfig) -> BenchmarkResult:
   env_cfg.scene.num_envs = cfg.num_envs
 
   # Handle tracking task motion file.
-  if env_cfg.commands is not None:
+  if len(env_cfg.commands) > 0:
     motion_cmd = env_cfg.commands.get("motion")
     if isinstance(motion_cmd, MotionCommandCfg):
       api = wandb.Api()
diff --git a/src/mjlab/envs/manager_based_rl_env.py b/src/mjlab/envs/manager_based_rl_env.py
index 67af64aa4..94de3807b 100644
--- a/src/mjlab/envs/manager_based_rl_env.py
+++ b/src/mjlab/envs/manager_based_rl_env.py
@@ -10,22 +10,21 @@
 
 from mjlab.envs import types
 from mjlab.envs.mdp.events import reset_scene_to_default
-from mjlab.managers.action_manager import ActionManager
-from mjlab.managers.command_manager import CommandManager, NullCommandManager
-from mjlab.managers.curriculum_manager import CurriculumManager, NullCurriculumManager
-from mjlab.managers.event_manager import EventManager
-from mjlab.managers.manager_term_config import (
-  ActionTermCfg,
+from mjlab.managers.action_manager import ActionManager, ActionTermCfg
+from mjlab.managers.command_manager import (
+  CommandManager,
   CommandTermCfg,
+  NullCommandManager,
+)
+from mjlab.managers.curriculum_manager import (
+  CurriculumManager,
   CurriculumTermCfg,
-  EventTermCfg,
-  ObservationGroupCfg,
-  RewardTermCfg,
-  TerminationTermCfg,
+  NullCurriculumManager,
 )
-from mjlab.managers.observation_manager import ObservationManager
-from mjlab.managers.reward_manager import RewardManager
-from mjlab.managers.termination_manager import TerminationManager
+from mjlab.managers.event_manager import EventManager, EventTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationManager
+from mjlab.managers.reward_manager import RewardManager, RewardTermCfg
+from mjlab.managers.termination_manager import TerminationManager, TerminationTermCfg
 from mjlab.scene import Scene
 from mjlab.scene.scene import SceneCfg
 from mjlab.sim import SimulationCfg
@@ -41,14 +40,36 @@
 
 @dataclass(kw_only=True)
 class ManagerBasedRlEnvCfg:
-  """Configuration for a manager-based RL environment."""
+  """Configuration for a manager-based RL environment.
+
+  This config defines all aspects of an RL environment: the physical scene,
+  observations, actions, rewards, terminations, and optional features like
+  commands and curriculum learning.
+
+  The environment step size is ``sim.mujoco.timestep * decimation``. For example,
+  with a 2ms physics timestep and decimation=10, the environment runs at 50Hz.
+  """
 
   # Base environment configuration.
+
   decimation: int
-  """Number of simulation steps per environment step."""
+  """Number of physics simulation steps per environment step. Higher values mean
+  coarser control frequency. Environment step duration = physics_dt * decimation."""
+
   scene: SceneCfg
+  """Scene configuration defining terrain, entities, and sensors. The scene
+  specifies ``num_envs``, the number of parallel environments."""
+
   observations: dict[str, ObservationGroupCfg]
+  """Observation groups configuration. Each group (e.g., "policy", "critic") contains
+  observation terms that are concatenated. Groups can have different settings for
+  noise, history, and delay. Can be empty for environments without observations."""
+
   actions: dict[str, ActionTermCfg]
+  """Action terms configuration. Each term controls a specific entity/aspect
+  (e.g., joint positions). Action dimensions are concatenated across terms.
+  Can be empty for observation-only environments."""
+
   events: dict[str, EventTermCfg] = field(
     default_factory=lambda: {
       "reset_scene_to_default": EventTermCfg(
@@ -57,25 +78,44 @@ class ManagerBasedRlEnvCfg:
       )
     }
   )
+  """Event terms for domain randomization and state resets. Default includes
+  ``reset_scene_to_default`` which resets entities to their initial state.
+  Can be set to empty to disable all events including default reset."""
+
   seed: int | None = None
+  """Random seed for reproducibility. If None, a random seed is used. The actual
+  seed used is stored back into this field after initialization."""
+
   sim: SimulationCfg = field(default_factory=SimulationCfg)
+  """Simulation configuration including physics timestep, solver iterations,
+  contact parameters, and NaN guarding."""
+
   viewer: ViewerConfig = field(default_factory=ViewerConfig)
+  """Viewer configuration for rendering (camera position, resolution, etc.)."""
 
   # RL-specific configuration.
+
   episode_length_s: float = 0.0
   """Duration of an episode (in seconds).
 
   Episode length in steps is computed as:
     ceil(episode_length_s / (sim.mujoco.timestep * decimation))
   """
+
   rewards: dict[str, RewardTermCfg] = field(default_factory=dict)
-  """Reward terms configuration."""
+  """Reward terms configuration. Can be empty for unsupervised environments."""
+
   terminations: dict[str, TerminationTermCfg] = field(default_factory=dict)
-  """Termination terms configuration."""
-  commands: dict[str, CommandTermCfg] | None = None
-  """Command terms configuration. If None, no commands are used."""
-  curriculum: dict[str, CurriculumTermCfg] | None = None
-  """Curriculum terms configuration. If None, no curriculum is used."""
+  """Termination terms configuration. Can be empty for infinite episodes (no
+  terminations). Use ``mdp.time_out`` with ``time_out=True`` for episode time limits."""
+
+  commands: dict[str, CommandTermCfg] = field(default_factory=dict)
+  """Command generator terms (e.g., velocity targets). Can be empty if the
+  task has no goal commands."""
+
+  curriculum: dict[str, CurriculumTermCfg] = field(default_factory=dict)
+  """Curriculum terms for adaptive difficulty. Can be empty to disable."""
+
   is_finite_horizon: bool = False
   """Whether the task has a finite or infinite horizon. Defaults to False (infinite).
 
@@ -85,6 +125,7 @@ class ManagerBasedRlEnvCfg:
     receives a truncated done signal to bootstrap the value of continuing beyond the
     limit.
   """
+
   scale_rewards_by_dt: bool = True
   """Whether to multiply rewards by the environment step duration (dt).
 
@@ -227,7 +268,7 @@ def load_managers(self) -> None:
 
     # Command manager (must be before observation manager since observations
     # may reference commands).
-    if self.cfg.commands is not None:
+    if len(self.cfg.commands) > 0:
       self.command_manager = CommandManager(self.cfg.commands, self)
     else:
       self.command_manager = NullCommandManager()
@@ -247,7 +288,7 @@ def load_managers(self) -> None:
       self.cfg.rewards, self, scale_by_dt=self.cfg.scale_rewards_by_dt
     )
     print_info(f"[INFO] {self.reward_manager}")
-    if self.cfg.curriculum is not None:
+    if len(self.cfg.curriculum) > 0:
       self.curriculum_manager = CurriculumManager(self.cfg.curriculum, self)
     else:
       self.curriculum_manager = NullCurriculumManager()
diff --git a/src/mjlab/envs/mdp/actions/actions.py b/src/mjlab/envs/mdp/actions/actions.py
index b06626a22..72354fc06 100644
--- a/src/mjlab/envs/mdp/actions/actions.py
+++ b/src/mjlab/envs/mdp/actions/actions.py
@@ -8,8 +8,7 @@
 import torch
 
 from mjlab.actuator.actuator import TransmissionType
-from mjlab.managers.action_manager import ActionTerm
-from mjlab.managers.manager_term_config import ActionTermCfg
+from mjlab.managers.action_manager import ActionTerm, ActionTermCfg
 from mjlab.utils.lab_api.string import resolve_matching_names_values
 
 if TYPE_CHECKING:
diff --git a/src/mjlab/envs/mdp/rewards.py b/src/mjlab/envs/mdp/rewards.py
index af2882a66..edfe5a4f1 100644
--- a/src/mjlab/envs/mdp/rewards.py
+++ b/src/mjlab/envs/mdp/rewards.py
@@ -7,7 +7,7 @@
 import torch
 
 from mjlab.entity import Entity
-from mjlab.managers.manager_term_config import RewardTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
 from mjlab.utils.lab_api.string import (
   resolve_matching_names_values,
diff --git a/src/mjlab/managers/__init__.py b/src/mjlab/managers/__init__.py
index 621d63f1f..114f198c1 100644
--- a/src/mjlab/managers/__init__.py
+++ b/src/mjlab/managers/__init__.py
@@ -1,10 +1,30 @@
 """Environment managers."""
 
+from mjlab.managers.action_manager import ActionManager as ActionManager
+from mjlab.managers.action_manager import ActionTerm as ActionTerm
+from mjlab.managers.action_manager import ActionTermCfg as ActionTermCfg
 from mjlab.managers.command_manager import CommandManager as CommandManager
 from mjlab.managers.command_manager import CommandTerm as CommandTerm
+from mjlab.managers.command_manager import CommandTermCfg as CommandTermCfg
 from mjlab.managers.command_manager import NullCommandManager as NullCommandManager
 from mjlab.managers.curriculum_manager import CurriculumManager as CurriculumManager
+from mjlab.managers.curriculum_manager import CurriculumTermCfg as CurriculumTermCfg
 from mjlab.managers.curriculum_manager import (
   NullCurriculumManager as NullCurriculumManager,
 )
-from mjlab.managers.manager_term_config import CommandTermCfg as CommandTermCfg
+from mjlab.managers.event_manager import EventManager as EventManager
+from mjlab.managers.event_manager import EventMode as EventMode
+from mjlab.managers.event_manager import EventTermCfg as EventTermCfg
+from mjlab.managers.manager_base import ManagerBase as ManagerBase
+from mjlab.managers.manager_base import ManagerTermBase as ManagerTermBase
+from mjlab.managers.manager_base import ManagerTermBaseCfg as ManagerTermBaseCfg
+from mjlab.managers.observation_manager import (
+  ObservationGroupCfg as ObservationGroupCfg,
+)
+from mjlab.managers.observation_manager import ObservationManager as ObservationManager
+from mjlab.managers.observation_manager import ObservationTermCfg as ObservationTermCfg
+from mjlab.managers.reward_manager import RewardManager as RewardManager
+from mjlab.managers.reward_manager import RewardTermCfg as RewardTermCfg
+from mjlab.managers.scene_entity_config import SceneEntityCfg as SceneEntityCfg
+from mjlab.managers.termination_manager import TerminationManager as TerminationManager
+from mjlab.managers.termination_manager import TerminationTermCfg as TerminationTermCfg
diff --git a/src/mjlab/managers/action_manager.py b/src/mjlab/managers/action_manager.py
index e5daa3086..4bd936f01 100644
--- a/src/mjlab/managers/action_manager.py
+++ b/src/mjlab/managers/action_manager.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import abc
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Sequence
 
 import torch
@@ -12,7 +13,27 @@
 
 if TYPE_CHECKING:
   from mjlab.envs import ManagerBasedRlEnv
-  from mjlab.managers.manager_term_config import ActionTermCfg
+
+
+@dataclass(kw_only=True)
+class ActionTermCfg(abc.ABC):
+  """Configuration for an action term.
+
+  Action terms process raw actions from the policy and apply them to entities
+  in the scene (e.g., setting joint positions, velocities, or efforts).
+  """
+
+  entity_name: str
+  """Name of the entity in the scene that this action term controls."""
+
+  clip: dict[str, tuple] | None = None
+  """Optional clipping bounds per transmission type. Maps transmission name
+  (e.g., 'position', 'velocity') to (min, max) tuple."""
+
+  @abc.abstractmethod
+  def build(self, env: ManagerBasedRlEnv) -> ActionTerm:
+    """Build the action term from this config."""
+    raise NotImplementedError
 
 
 class ActionTerm(ManagerTermBase):
@@ -47,6 +68,13 @@ def raw_action(self) -> torch.Tensor:
 
 
 class ActionManager(ManagerBase):
+  """Manages action processing for the environment.
+
+  The action manager aggregates multiple action terms, each controlling a different
+  entity or aspect of the simulation. It splits the policy's action tensor and
+  routes each slice to the appropriate action term.
+  """
+
   def __init__(self, cfg: dict[str, ActionTermCfg], env: ManagerBasedRlEnv):
     self.cfg = cfg
     super().__init__(env=env)
diff --git a/src/mjlab/managers/command_manager.py b/src/mjlab/managers/command_manager.py
index bc6881929..b044a08e8 100644
--- a/src/mjlab/managers/command_manager.py
+++ b/src/mjlab/managers/command_manager.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import abc
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Sequence
 
 import torch
@@ -12,10 +13,34 @@
 
 if TYPE_CHECKING:
   from mjlab.envs.manager_based_rl_env import ManagerBasedRlEnv
-  from mjlab.managers.manager_term_config import CommandTermCfg
   from mjlab.viewer.debug_visualizer import DebugVisualizer
 
 
+@dataclass(kw_only=True)
+class CommandTermCfg(abc.ABC):
+  """Configuration for a command generator term.
+
+  Command terms generate goal commands for the agent (e.g., target velocity,
+  target position). Commands are automatically resampled at configurable
+  intervals and can track metrics for logging.
+  """
+
+  resampling_time_range: tuple[float, float]
+  """Time range in seconds for command resampling. When the timer expires, a new
+  command is sampled and the timer is reset to a value uniformly drawn from
+  ``[min, max]``. Set both values equal for fixed-interval resampling."""
+
+  debug_vis: bool = False
+  """Whether to enable debug visualization for this command term. When True,
+  the command term's ``_debug_vis_impl`` method is called each frame to render
+  visual aids (e.g., velocity arrows, target markers)."""
+
+  @abc.abstractmethod
+  def build(self, env: ManagerBasedRlEnv) -> CommandTerm:
+    """Build the command term from this config."""
+    raise NotImplementedError
+
+
 class CommandTerm(ManagerTermBase):
   """Base class for command terms."""
 
@@ -83,6 +108,13 @@ def _update_command(self) -> None:
 
 
 class CommandManager(ManagerBase):
+  """Manages command generation for the environment.
+
+  The command manager generates and updates goal commands for the agent (e.g.,
+  target velocity, target position). Commands are resampled at configurable
+  intervals and can track metrics for logging.
+  """
+
   _env: ManagerBasedRlEnv
 
   def __init__(self, cfg: dict[str, CommandTermCfg], env: ManagerBasedRlEnv):
diff --git a/src/mjlab/managers/curriculum_manager.py b/src/mjlab/managers/curriculum_manager.py
index cd180c063..d34b25aa6 100644
--- a/src/mjlab/managers/curriculum_manager.py
+++ b/src/mjlab/managers/curriculum_manager.py
@@ -3,19 +3,37 @@
 from __future__ import annotations
 
 from copy import deepcopy
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Sequence
 
 import torch
 from prettytable import PrettyTable
 
-from mjlab.managers.manager_base import ManagerBase
-from mjlab.managers.manager_term_config import CurriculumTermCfg
+from mjlab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
 
 if TYPE_CHECKING:
   from mjlab.envs.manager_based_rl_env import ManagerBasedRlEnv
 
 
+@dataclass(kw_only=True)
+class CurriculumTermCfg(ManagerTermBaseCfg):
+  """Configuration for a curriculum term.
+
+  Curriculum terms modify environment parameters during training to implement
+  curriculum learning strategies (e.g., gradually increasing task difficulty).
+  """
+
+  pass
+
+
 class CurriculumManager(ManagerBase):
+  """Manages curriculum learning for the environment.
+
+  The curriculum manager updates environment parameters during training based
+  on agent performance. Each term can modify different aspects of the task
+  difficulty (e.g., terrain complexity, command ranges).
+  """
+
   _env: ManagerBasedRlEnv
 
   def __init__(self, cfg: dict[str, CurriculumTermCfg], env: ManagerBasedRlEnv):
diff --git a/src/mjlab/managers/event_manager.py b/src/mjlab/managers/event_manager.py
index 03ab162db..98c256866 100644
--- a/src/mjlab/managers/event_manager.py
+++ b/src/mjlab/managers/event_manager.py
@@ -3,19 +3,74 @@
 from __future__ import annotations
 
 from copy import deepcopy
-from typing import TYPE_CHECKING
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Literal
 
 import torch
 from prettytable import PrettyTable
 
-from mjlab.managers.manager_base import ManagerBase
-from mjlab.managers.manager_term_config import EventMode, EventTermCfg
+from mjlab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
 
 if TYPE_CHECKING:
   from mjlab.envs import ManagerBasedRlEnv
 
 
+EventMode = Literal["startup", "reset", "interval"]
+
+
+@dataclass(kw_only=True)
+class EventTermCfg(ManagerTermBaseCfg):
+  """Configuration for an event term.
+
+  Event terms trigger operations at specific simulation events. They're commonly
+  used for domain randomization, state resets, and periodic perturbations.
+
+  The three modes determine when the event fires:
+
+  - ``"startup"``: Once when the environment initializes. Use for parameters that
+    should be randomized per-environment but stay constant within an episode (
+    e.g., domain randomization).
+
+  - ``"reset"``: On every episode reset. Use for parameters that should vary
+    between episodes (e.g., initial robot pose, domain randomization).
+
+  - ``"interval"``: Periodically during simulation, controlled by
+    ``interval_range_s``. Use for perturbations that should happen during
+    episodes (e.g., pushing the robot, external disturbances).
+  """
+
+  mode: EventMode
+  """When the event triggers: ``"startup"`` (once at init), ``"reset"`` (every
+  episode), or ``"interval"`` (periodically during simulation)."""
+
+  interval_range_s: tuple[float, float] | None = None
+  """Time range in seconds for interval mode. The next trigger time is uniformly
+  sampled from ``[min, max]``. Required when ``mode="interval"``."""
+
+  is_global_time: bool = False
+  """Whether all environments share the same timer. If True, all envs trigger
+  simultaneously. If False (default), each env has an independent timer that
+  resets on episode reset. Only applies to ``mode="interval"``."""
+
+  min_step_count_between_reset: int = 0
+  """Minimum environment steps between triggers. Prevents the event from firing
+  too frequently when episodes reset rapidly. Only applies to ``mode="reset"``.
+  Set to 0 (default) to trigger on every reset."""
+
+  domain_randomization: bool = False
+  """Whether this event performs domain randomization. If True, the field name
+  from ``params["field"]`` is tracked and exposed via
+  ``EventManager.domain_randomization_fields`` for logging/debugging."""
+
+
 class EventManager(ManagerBase):
+  """Manages event-based operations for the environment.
+
+  The event manager triggers operations at different simulation events: startup
+  (once at initialization), reset (on episode reset), or interval (periodically
+  during simulation). Common uses include domain randomization and state resets.
+  """
+
   _env: ManagerBasedRlEnv
 
   def __init__(self, cfg: dict[str, EventTermCfg], env: ManagerBasedRlEnv):
diff --git a/src/mjlab/managers/manager_base.py b/src/mjlab/managers/manager_base.py
index ff4743636..049df2d4c 100644
--- a/src/mjlab/managers/manager_base.py
+++ b/src/mjlab/managers/manager_base.py
@@ -3,6 +3,7 @@
 import abc
 import inspect
 from collections.abc import Sequence
+from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any
 
 import torch
@@ -11,7 +12,53 @@
 
 if TYPE_CHECKING:
   from mjlab.envs import ManagerBasedRlEnv
-  from mjlab.managers.manager_term_config import ManagerTermBaseCfg
+
+
+@dataclass
+class ManagerTermBaseCfg:
+  """Base configuration for manager terms.
+
+  This is the base config for terms in observation, reward, termination, curriculum,
+  and event managers. It provides a common interface for specifying a callable
+  and its parameters.
+
+  The ``func`` field accepts either a function or a class:
+
+  **Function-based terms** are simpler and suitable for stateless computations:
+
+  .. code-block:: python
+
+      RewardTermCfg(func=mdp.joint_torques_l2, weight=-0.01)
+
+  **Class-based terms** are instantiated with ``(cfg, env)`` and useful when you need
+  to:
+
+  - Cache computed values at initialization (e.g., resolve regex patterns to indices)
+  - Maintain state across calls
+  - Perform expensive setup once rather than every call
+
+  .. code-block:: python
+
+      class posture:
+        def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRlEnv):
+          # Resolve std dict to tensor once at init
+          self.std = resolve_std_to_tensor(cfg.params["std"], env)
+
+        def __call__(self, env, **kwargs) -> torch.Tensor:
+          # Use cached self.std
+          return compute_posture_reward(env, self.std)
+
+      RewardTermCfg(func=posture, params={"std": {".*knee.*": 0.3}}, weight=1.0)
+
+  Class-based terms can optionally implement ``reset(env_ids)`` for per-episode state.
+  """
+
+  func: Any
+  """The callable that computes this term's value. Can be a function or a class.
+  Classes are auto-instantiated with ``(cfg=term_cfg, env=env)``."""
+
+  params: dict[str, Any] = field(default_factory=lambda: {})
+  """Additional keyword arguments passed to func when called."""
 
 
 class ManagerTermBase:
diff --git a/src/mjlab/managers/manager_term_config.py b/src/mjlab/managers/manager_term_config.py
deleted file mode 100644
index 6a5aac57c..000000000
--- a/src/mjlab/managers/manager_term_config.py
+++ /dev/null
@@ -1,174 +0,0 @@
-from __future__ import annotations
-
-import abc
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Literal
-
-import torch
-
-from mjlab.managers.command_manager import CommandTerm
-from mjlab.utils.noise.noise_cfg import NoiseCfg, NoiseModelCfg
-
-if TYPE_CHECKING:
-  from mjlab.envs import ManagerBasedRlEnv
-  from mjlab.managers.action_manager import ActionTerm
-
-
-@dataclass
-class ManagerTermBaseCfg:
-  func: Any
-  params: dict[str, Any] = field(default_factory=lambda: {})
-
-
-##
-# Action manager.
-##
-
-
-@dataclass(kw_only=True)
-class ActionTermCfg(abc.ABC):
-  """Configuration for an action term."""
-
-  entity_name: str
-  clip: dict[str, tuple] | None = None
-
-  @abc.abstractmethod
-  def build(self, env: ManagerBasedRlEnv) -> ActionTerm:
-    """Build the action term from this config."""
-    raise NotImplementedError
-
-
-##
-# Command manager.
-##
-
-
-@dataclass(kw_only=True)
-class CommandTermCfg(abc.ABC):
-  """Configuration for a command generator term."""
-
-  resampling_time_range: tuple[float, float]
-  debug_vis: bool = False
-
-  @abc.abstractmethod
-  def build(self, env: ManagerBasedRlEnv) -> CommandTerm:
-    """Build the command term from this config."""
-    raise NotImplementedError
-
-
-##
-# Curriculum manager.
-##
-
-
-@dataclass(kw_only=True)
-class CurriculumTermCfg(ManagerTermBaseCfg):
-  pass
-
-
-##
-# Event manager.
-##
-
-
-EventMode = Literal["startup", "reset", "interval"]
-
-
-@dataclass(kw_only=True)
-class EventTermCfg(ManagerTermBaseCfg):
-  """Configuration for an event term."""
-
-  mode: EventMode
-  interval_range_s: tuple[float, float] | None = None
-  is_global_time: bool = False
-  min_step_count_between_reset: int = 0
-  domain_randomization: bool = False
-  """Whether this event term performs domain randomization. If True, the field
-  name (from params["field"]) will be tracked for domain randomization purposes."""
-
-
-##
-# Observation manager.
-##
-
-
-@dataclass
-class ObservationTermCfg(ManagerTermBaseCfg):
-  """Configuration for an observation term.
-
-  Processing pipeline: compute → noise → clip → scale → delay → history.
-  Delay models sensor latency. History provides temporal context. Both are optional
-  and can be combined.
-  """
-
-  noise: NoiseCfg | NoiseModelCfg | None = None
-  """Noise model to apply to the observation."""
-  clip: tuple[float, float] | None = None
-  """Range (min, max) to clip the observation values."""
-  scale: tuple[float, ...] | float | torch.Tensor | None = None
-  """Scaling factor(s) to multiply the observation by."""
-  delay_min_lag: int = 0
-  """Minimum lag (in steps) for delayed observations. Lag sampled uniformly from
-  [min_lag, max_lag]. Convert to ms: lag * (1000 / control_hz)."""
-  delay_max_lag: int = 0
-  """Maximum lag (in steps) for delayed observations. Use min=max for constant delay."""
-  delay_per_env: bool = True
-  """If True, each environment samples its own lag. If False, all environments share
-  the same lag at each step."""
-  delay_hold_prob: float = 0.0
-  """Probability of reusing the previous lag instead of resampling. Useful for
-  temporally correlated latency patterns."""
-  delay_update_period: int = 0
-  """Resample lag every N steps (models multi-rate sensors). If 0, update every step."""
-  delay_per_env_phase: bool = True
-  """If True and update_period > 0, stagger update timing across envs to avoid
-  synchronized resampling."""
-  history_length: int = 0
-  """Number of past observations to keep in history. 0 = no history."""
-  flatten_history_dim: bool = True
-  """Whether to flatten the history dimension into observation.
-
-  When True and concatenate_terms=True, uses term-major ordering:
-  [A_t0, A_t1, ..., A_tH-1, B_t0, B_t1, ..., B_tH-1, ...]
-  See docs/api/observation_history_delay.md for details on ordering."""
-
-
-@dataclass
-class ObservationGroupCfg:
-  """Configuration for an observation group.
-
-  The `terms` field contains a dictionary mapping term names to their configurations.
-  """
-
-  terms: dict[str, ObservationTermCfg]
-  concatenate_terms: bool = True
-  concatenate_dim: int = -1
-  enable_corruption: bool = False
-  history_length: int | None = None
-  flatten_history_dim: bool = True
-
-
-##
-# Reward manager.
-##
-
-
-@dataclass(kw_only=True)
-class RewardTermCfg(ManagerTermBaseCfg):
-  """Configuration for a reward term."""
-
-  func: Any
-  weight: float
-
-
-##
-# Termination manager.
-##
-
-
-@dataclass
-class TerminationTermCfg(ManagerTermBaseCfg):
-  """Configuration for a termination term."""
-
-  time_out: bool = False
-  """Whether the term contributes towards episodic timeouts."""
diff --git a/src/mjlab/managers/observation_manager.py b/src/mjlab/managers/observation_manager.py
index 026b48d00..311d30309 100644
--- a/src/mjlab/managers/observation_manager.py
+++ b/src/mjlab/managers/observation_manager.py
@@ -1,19 +1,111 @@
 """Observation manager for computing observations."""
 
 from copy import deepcopy
+from dataclasses import dataclass
 from typing import Sequence
 
 import numpy as np
 import torch
 from prettytable import PrettyTable
 
-from mjlab.managers.manager_base import ManagerBase
-from mjlab.managers.manager_term_config import ObservationGroupCfg, ObservationTermCfg
+from mjlab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
 from mjlab.utils.buffers import CircularBuffer, DelayBuffer
 from mjlab.utils.noise import noise_cfg, noise_model
+from mjlab.utils.noise.noise_cfg import NoiseCfg, NoiseModelCfg
+
+
+@dataclass
+class ObservationTermCfg(ManagerTermBaseCfg):
+  """Configuration for an observation term.
+
+  Processing pipeline: compute → noise → clip → scale → delay → history.
+  Delay models sensor latency. History provides temporal context. Both are optional
+  and can be combined.
+  """
+
+  noise: NoiseCfg | NoiseModelCfg | None = None
+  """Noise model to apply to the observation."""
+
+  clip: tuple[float, float] | None = None
+  """Range (min, max) to clip the observation values."""
+
+  scale: tuple[float, ...] | float | torch.Tensor | None = None
+  """Scaling factor(s) to multiply the observation by."""
+
+  delay_min_lag: int = 0
+  """Minimum lag (in steps) for delayed observations. Lag sampled uniformly from
+  [min_lag, max_lag]. Convert to ms: lag * (1000 / control_hz)."""
+
+  delay_max_lag: int = 0
+  """Maximum lag (in steps) for delayed observations. Use min=max for constant delay."""
+
+  delay_per_env: bool = True
+  """If True, each environment samples its own lag. If False, all environments share
+  the same lag at each step."""
+
+  delay_hold_prob: float = 0.0
+  """Probability of reusing the previous lag instead of resampling. Useful for
+  temporally correlated latency patterns."""
+
+  delay_update_period: int = 0
+  """Resample lag every N steps (models multi-rate sensors). If 0, update every step."""
+
+  delay_per_env_phase: bool = True
+  """If True and update_period > 0, stagger update timing across envs to avoid
+  synchronized resampling."""
+
+  history_length: int = 0
+  """Number of past observations to keep in history. 0 = no history."""
+
+  flatten_history_dim: bool = True
+  """Whether to flatten the history dimension into observation.
+
+  When True and concatenate_terms=True, uses term-major ordering:
+  [A_t0, A_t1, ..., A_tH-1, B_t0, B_t1, ..., B_tH-1, ...]
+  See docs/api/observation_history_delay.md for details on ordering."""
+
+
+@dataclass
+class ObservationGroupCfg:
+  """Configuration for an observation group.
+
+  An observation group bundles multiple observation terms together. Groups are
+  typically used to separate observations for different purposes (e.g., "policy"
+  for the actor, "critic" for the value function).
+  """
+
+  terms: dict[str, ObservationTermCfg]
+  """Dictionary mapping term names to their configurations."""
+
+  concatenate_terms: bool = True
+  """Whether to concatenate all terms into a single tensor. If False, returns
+  a dict mapping term names to their individual tensors."""
+
+  concatenate_dim: int = -1
+  """Dimension along which to concatenate terms. Default -1 (last dimension)."""
+
+  enable_corruption: bool = False
+  """Whether to apply noise corruption to observations. Set to True during
+  training for domain randomization, False during evaluation."""
+
+  history_length: int | None = None
+  """Group-level history length override. If set, applies to all terms in
+  this group. If None, each term uses its own ``history_length`` setting."""
+
+  flatten_history_dim: bool = True
+  """Whether to flatten history into the observation dimension. If True,
+  observations have shape ``(num_envs, obs_dim * history_length)``. If False,
+  shape is ``(num_envs, history_length, obs_dim)``."""
 
 
 class ObservationManager(ManagerBase):
+  """Manages observation computation for the environment.
+
+  The observation manager computes observations from multiple terms organized
+  into groups. Each term can have noise, clipping, scaling, delay, and history
+  applied. Groups can optionally concatenate their terms into a single tensor.
+  """
+
   def __init__(self, cfg: dict[str, ObservationGroupCfg], env):
     self.cfg = deepcopy(cfg)
     super().__init__(env=env)
diff --git a/src/mjlab/managers/reward_manager.py b/src/mjlab/managers/reward_manager.py
index 51cf8dfd0..6afb5c7b6 100644
--- a/src/mjlab/managers/reward_manager.py
+++ b/src/mjlab/managers/reward_manager.py
@@ -3,18 +3,29 @@
 from __future__ import annotations
 
 from copy import deepcopy
-from typing import TYPE_CHECKING
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
 
 import torch
 from prettytable import PrettyTable
 
-from mjlab.managers.manager_base import ManagerBase
-from mjlab.managers.manager_term_config import RewardTermCfg
+from mjlab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
 
 if TYPE_CHECKING:
   from mjlab.envs.manager_based_rl_env import ManagerBasedRlEnv
 
 
+@dataclass(kw_only=True)
+class RewardTermCfg(ManagerTermBaseCfg):
+  """Configuration for a reward term."""
+
+  func: Any
+  """The callable that computes this reward term's value."""
+
+  weight: float
+  """Weight multiplier for this reward term."""
+
+
 class RewardManager(ManagerBase):
   """Manages reward computation by aggregating weighted reward terms.
 
diff --git a/src/mjlab/managers/termination_manager.py b/src/mjlab/managers/termination_manager.py
index baac956fc..d31808609 100644
--- a/src/mjlab/managers/termination_manager.py
+++ b/src/mjlab/managers/termination_manager.py
@@ -3,19 +3,34 @@
 from __future__ import annotations
 
 from copy import deepcopy
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Sequence
 
 import torch
 from prettytable import PrettyTable
 
-from mjlab.managers.manager_base import ManagerBase
-from mjlab.managers.manager_term_config import TerminationTermCfg
+from mjlab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
 
 if TYPE_CHECKING:
   from mjlab.envs.manager_based_rl_env import ManagerBasedRlEnv
 
 
+@dataclass
+class TerminationTermCfg(ManagerTermBaseCfg):
+  """Configuration for a termination term."""
+
+  time_out: bool = False
+  """Whether the term contributes towards episodic timeouts."""
+
+
 class TerminationManager(ManagerBase):
+  """Manages termination conditions for the environment.
+
+  The termination manager aggregates multiple termination terms to compute
+  episode done signals. Terms can be either truncations (time-based) or
+  terminations (failure conditions).
+  """
+
   _env: ManagerBasedRlEnv
 
   def __init__(self, cfg: dict[str, TerminationTermCfg], env: ManagerBasedRlEnv):
diff --git a/src/mjlab/scripts/play.py b/src/mjlab/scripts/play.py
index 4d385b5e3..b905edb8d 100644
--- a/src/mjlab/scripts/play.py
+++ b/src/mjlab/scripts/play.py
@@ -52,21 +52,17 @@ def run_play(task_id: str, cfg: PlayConfig):
   TRAINED_MODE = not DUMMY_MODE
 
   # Check if this is a tracking task by checking for motion command.
-  is_tracking_task = (
-    env_cfg.commands is not None
-    and "motion" in env_cfg.commands
-    and isinstance(env_cfg.commands["motion"], MotionCommandCfg)
+  is_tracking_task = "motion" in env_cfg.commands and isinstance(
+    env_cfg.commands["motion"], MotionCommandCfg
   )
 
   if is_tracking_task and cfg._demo_mode:
     # Demo mode: use uniform sampling to see more diversity with num_envs > 1.
-    assert env_cfg.commands is not None
     motion_cmd = env_cfg.commands["motion"]
     assert isinstance(motion_cmd, MotionCommandCfg)
     motion_cmd.sampling_mode = "uniform"
 
   if is_tracking_task:
-    assert env_cfg.commands is not None
     motion_cmd = env_cfg.commands["motion"]
     assert isinstance(motion_cmd, MotionCommandCfg)
 
diff --git a/src/mjlab/scripts/train.py b/src/mjlab/scripts/train.py
index 1a845dd46..69f1503b3 100644
--- a/src/mjlab/scripts/train.py
+++ b/src/mjlab/scripts/train.py
@@ -68,10 +68,8 @@ def run_train(task_id: str, cfg: TrainConfig, log_dir: Path) -> None:
   registry_name: str | None = None
 
   # Check if this is a tracking task by checking for motion command.
-  is_tracking_task = (
-    cfg.env.commands is not None
-    and "motion" in cfg.env.commands
-    and isinstance(cfg.env.commands["motion"], MotionCommandCfg)
+  is_tracking_task = "motion" in cfg.env.commands and isinstance(
+    cfg.env.commands["motion"], MotionCommandCfg
   )
 
   if is_tracking_task:
@@ -87,7 +85,6 @@ def run_train(task_id: str, cfg: TrainConfig, log_dir: Path) -> None:
     api = wandb.Api()
     artifact = api.artifact(registry_name)
 
-    assert cfg.env.commands is not None
     motion_cmd = cfg.env.commands["motion"]
     assert isinstance(motion_cmd, MotionCommandCfg)
     motion_cmd.motion_file = str(Path(artifact.download()) / "motion.npz")
diff --git a/src/mjlab/tasks/manipulation/config/yam/env_cfgs.py b/src/mjlab/tasks/manipulation/config/yam/env_cfgs.py
index 5a962e95e..58684c1c6 100644
--- a/src/mjlab/tasks/manipulation/config/yam/env_cfgs.py
+++ b/src/mjlab/tasks/manipulation/config/yam/env_cfgs.py
@@ -40,7 +40,6 @@ def yam_lift_cube_env_cfg(
   assert isinstance(joint_pos_action, JointPositionActionCfg)
   joint_pos_action.scale = YAM_ACTION_SCALE
 
-  assert cfg.commands is not None
   lift_command = cfg.commands["lift_height"]
   assert isinstance(lift_command, LiftingCommandCfg)
 
diff --git a/src/mjlab/tasks/manipulation/lift_cube_env_cfg.py b/src/mjlab/tasks/manipulation/lift_cube_env_cfg.py
index 2e4ddcece..0554fcb01 100644
--- a/src/mjlab/tasks/manipulation/lift_cube_env_cfg.py
+++ b/src/mjlab/tasks/manipulation/lift_cube_env_cfg.py
@@ -1,16 +1,13 @@
 from mjlab.envs import ManagerBasedRlEnvCfg
 from mjlab.envs.mdp.actions import JointPositionActionCfg
-from mjlab.managers.manager_term_config import (
-  ActionTermCfg,
-  CommandTermCfg,
-  CurriculumTermCfg,
-  EventTermCfg,
-  ObservationGroupCfg,
-  ObservationTermCfg,
-  RewardTermCfg,
-  TerminationTermCfg,
-)
+from mjlab.managers.action_manager import ActionTermCfg
+from mjlab.managers.command_manager import CommandTermCfg
+from mjlab.managers.curriculum_manager import CurriculumTermCfg
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.managers.termination_manager import TerminationTermCfg
 from mjlab.scene import SceneCfg
 from mjlab.sensor import ContactMatch, ContactSensorCfg
 from mjlab.sim import MujocoCfg, SimulationCfg
diff --git a/src/mjlab/tasks/manipulation/mdp/commands.py b/src/mjlab/tasks/manipulation/mdp/commands.py
index 32764595a..b0877563e 100644
--- a/src/mjlab/tasks/manipulation/mdp/commands.py
+++ b/src/mjlab/tasks/manipulation/mdp/commands.py
@@ -7,8 +7,7 @@
 import torch
 
 from mjlab.entity import Entity
-from mjlab.managers.command_manager import CommandTerm
-from mjlab.managers.manager_term_config import CommandTermCfg
+from mjlab.managers.command_manager import CommandTerm, CommandTermCfg
 from mjlab.utils.lab_api.math import (
   quat_from_euler_xyz,
   sample_uniform,
diff --git a/src/mjlab/tasks/tracking/config/g1/env_cfgs.py b/src/mjlab/tasks/tracking/config/g1/env_cfgs.py
index 2f63e02b3..563eae90d 100644
--- a/src/mjlab/tasks/tracking/config/g1/env_cfgs.py
+++ b/src/mjlab/tasks/tracking/config/g1/env_cfgs.py
@@ -6,7 +6,7 @@
 )
 from mjlab.envs import ManagerBasedRlEnvCfg
 from mjlab.envs.mdp.actions import JointPositionActionCfg
-from mjlab.managers.manager_term_config import ObservationGroupCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg
 from mjlab.sensor import ContactMatch, ContactSensorCfg
 from mjlab.tasks.tracking.mdp import MotionCommandCfg
 from mjlab.tasks.tracking.tracking_env_cfg import make_tracking_env_cfg
@@ -35,7 +35,6 @@ def unitree_g1_flat_tracking_env_cfg(
   assert isinstance(joint_pos_action, JointPositionActionCfg)
   joint_pos_action.scale = G1_ACTION_SCALE
 
-  assert cfg.commands is not None
   motion_cmd = cfg.commands["motion"]
   assert isinstance(motion_cmd, MotionCommandCfg)
   motion_cmd.anchor_body_name = "torso_link"
diff --git a/src/mjlab/tasks/tracking/scripts/evaluate.py b/src/mjlab/tasks/tracking/scripts/evaluate.py
index e7a8d94d0..1983802f2 100644
--- a/src/mjlab/tasks/tracking/scripts/evaluate.py
+++ b/src/mjlab/tasks/tracking/scripts/evaluate.py
@@ -52,7 +52,6 @@ def run_evaluate(task_id: str, cfg: EvaluateConfig) -> dict[str, float]:
   env_cfg = load_env_cfg(task_id, play=False)
   agent_cfg = load_rl_cfg(task_id)
 
-  assert env_cfg.commands is not None
   motion_cmd = env_cfg.commands.get("motion")
   if not isinstance(motion_cmd, MotionCommandCfg):
     raise ValueError(f"Task {task_id} is not a tracking task.")
diff --git a/src/mjlab/tasks/tracking/tracking_env_cfg.py b/src/mjlab/tasks/tracking/tracking_env_cfg.py
index adadabf08..ed978861f 100644
--- a/src/mjlab/tasks/tracking/tracking_env_cfg.py
+++ b/src/mjlab/tasks/tracking/tracking_env_cfg.py
@@ -11,16 +11,13 @@
 
 from mjlab.envs import ManagerBasedRlEnvCfg
 from mjlab.envs.mdp.actions import JointPositionActionCfg
-from mjlab.managers.manager_term_config import (
-  ActionTermCfg,
-  CommandTermCfg,
-  EventTermCfg,
-  ObservationGroupCfg,
-  ObservationTermCfg,
-  RewardTermCfg,
-  TerminationTermCfg,
-)
+from mjlab.managers.action_manager import ActionTermCfg
+from mjlab.managers.command_manager import CommandTermCfg
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.managers.termination_manager import TerminationTermCfg
 from mjlab.scene import SceneCfg
 from mjlab.sim import MujocoCfg, SimulationCfg
 from mjlab.tasks.tracking import mdp
diff --git a/src/mjlab/tasks/velocity/config/g1/env_cfgs.py b/src/mjlab/tasks/velocity/config/g1/env_cfgs.py
index 8df322225..2b093afa5 100644
--- a/src/mjlab/tasks/velocity/config/g1/env_cfgs.py
+++ b/src/mjlab/tasks/velocity/config/g1/env_cfgs.py
@@ -7,10 +7,8 @@
 from mjlab.envs import ManagerBasedRlEnvCfg
 from mjlab.envs import mdp as envs_mdp
 from mjlab.envs.mdp.actions import JointPositionActionCfg
-from mjlab.managers.manager_term_config import (
-  EventTermCfg,
-  RewardTermCfg,
-)
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
 from mjlab.sensor import ContactMatch, ContactSensorCfg
 from mjlab.tasks.velocity import mdp
 from mjlab.tasks.velocity.mdp import UniformVelocityCommandCfg
@@ -60,7 +58,6 @@ def unitree_g1_rough_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
 
   cfg.viewer.body_name = "torso_link"
 
-  assert cfg.commands is not None
   twist_cmd = cfg.commands["twist"]
   assert isinstance(twist_cmd, UniformVelocityCommandCfg)
   twist_cmd.viz.z_offset = 1.15
@@ -169,14 +166,11 @@ def unitree_g1_flat_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
   cfg.scene.terrain.terrain_generator = None
 
   # Disable terrain curriculum.
-  assert cfg.curriculum is not None
   assert "terrain_levels" in cfg.curriculum
   del cfg.curriculum["terrain_levels"]
 
   if play:
-    commands = cfg.commands
-    assert commands is not None
-    twist_cmd = commands["twist"]
+    twist_cmd = cfg.commands["twist"]
     assert isinstance(twist_cmd, UniformVelocityCommandCfg)
     twist_cmd.ranges.lin_vel_x = (-1.5, 2.0)
     twist_cmd.ranges.ang_vel_z = (-0.7, 0.7)
diff --git a/src/mjlab/tasks/velocity/config/go1/env_cfgs.py b/src/mjlab/tasks/velocity/config/go1/env_cfgs.py
index 17e3d039a..fc7d377d5 100644
--- a/src/mjlab/tasks/velocity/config/go1/env_cfgs.py
+++ b/src/mjlab/tasks/velocity/config/go1/env_cfgs.py
@@ -6,7 +6,7 @@
 )
 from mjlab.envs import ManagerBasedRlEnvCfg
 from mjlab.envs.mdp.actions import JointPositionActionCfg
-from mjlab.managers.manager_term_config import TerminationTermCfg
+from mjlab.managers.termination_manager import TerminationTermCfg
 from mjlab.sensor import ContactMatch, ContactSensorCfg
 from mjlab.tasks.velocity import mdp
 from mjlab.tasks.velocity.velocity_env_cfg import make_velocity_env_cfg
@@ -122,7 +122,6 @@ def unitree_go1_flat_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
   cfg.scene.terrain.terrain_generator = None
 
   # Disable terrain curriculum.
-  assert cfg.curriculum is not None
   del cfg.curriculum["terrain_levels"]
 
   return cfg
diff --git a/src/mjlab/tasks/velocity/mdp/rewards.py b/src/mjlab/tasks/velocity/mdp/rewards.py
index 265044af0..ff0199e53 100644
--- a/src/mjlab/tasks/velocity/mdp/rewards.py
+++ b/src/mjlab/tasks/velocity/mdp/rewards.py
@@ -5,7 +5,7 @@
 import torch
 
 from mjlab.entity import Entity
-from mjlab.managers.manager_term_config import RewardTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
 from mjlab.sensor import BuiltinSensor, ContactSensor
 from mjlab.utils.lab_api.math import quat_apply_inverse
diff --git a/src/mjlab/tasks/velocity/mdp/velocity_command.py b/src/mjlab/tasks/velocity/mdp/velocity_command.py
index e420a390d..346751b20 100644
--- a/src/mjlab/tasks/velocity/mdp/velocity_command.py
+++ b/src/mjlab/tasks/velocity/mdp/velocity_command.py
@@ -7,8 +7,7 @@
 import torch
 
 from mjlab.entity import Entity
-from mjlab.managers.command_manager import CommandTerm
-from mjlab.managers.manager_term_config import CommandTermCfg
+from mjlab.managers.command_manager import CommandTerm, CommandTermCfg
 from mjlab.utils.lab_api.math import (
   matrix_from_quat,
   quat_apply,
diff --git a/src/mjlab/tasks/velocity/velocity_env_cfg.py b/src/mjlab/tasks/velocity/velocity_env_cfg.py
index 34f2f6113..2be11cf48 100644
--- a/src/mjlab/tasks/velocity/velocity_env_cfg.py
+++ b/src/mjlab/tasks/velocity/velocity_env_cfg.py
@@ -9,17 +9,14 @@
 
 from mjlab.envs import ManagerBasedRlEnvCfg
 from mjlab.envs.mdp.actions import JointPositionActionCfg
-from mjlab.managers.manager_term_config import (
-  ActionTermCfg,
-  CommandTermCfg,
-  CurriculumTermCfg,
-  EventTermCfg,
-  ObservationGroupCfg,
-  ObservationTermCfg,
-  RewardTermCfg,
-  TerminationTermCfg,
-)
+from mjlab.managers.action_manager import ActionTermCfg
+from mjlab.managers.command_manager import CommandTermCfg
+from mjlab.managers.curriculum_manager import CurriculumTermCfg
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.managers.termination_manager import TerminationTermCfg
 from mjlab.scene import SceneCfg
 from mjlab.sim import MujocoCfg, SimulationCfg
 from mjlab.tasks.velocity import mdp
diff --git a/tests/test_encoder_bias.py b/tests/test_encoder_bias.py
index 2c5e4762c..5d7eb8f64 100644
--- a/tests/test_encoder_bias.py
+++ b/tests/test_encoder_bias.py
@@ -18,11 +18,8 @@
 from mjlab.actuator import BuiltinPositionActuatorCfg
 from mjlab.entity import EntityArticulationInfoCfg, EntityCfg
 from mjlab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, mdp
-from mjlab.managers.manager_term_config import (
-  EventTermCfg,
-  ObservationGroupCfg,
-  ObservationTermCfg,
-)
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
 from mjlab.scene import SceneCfg
 from mjlab.sim import MujocoCfg, SimulationCfg
diff --git a/tests/test_events.py b/tests/test_events.py
index 550a9022b..f2dc7ee47 100644
--- a/tests/test_events.py
+++ b/tests/test_events.py
@@ -8,8 +8,7 @@
 
 from mjlab import actuator
 from mjlab.envs.mdp import events
-from mjlab.managers.event_manager import EventManager
-from mjlab.managers.manager_term_config import EventTermCfg
+from mjlab.managers.event_manager import EventManager, EventTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
 
 
diff --git a/tests/test_manager_config_immutability.py b/tests/test_manager_config_immutability.py
index cfdf980f8..3bc17edc4 100644
--- a/tests/test_manager_config_immutability.py
+++ b/tests/test_manager_config_immutability.py
@@ -13,13 +13,12 @@
 from conftest import get_test_device
 
 from mjlab.entity import Entity, EntityCfg
-from mjlab.managers.manager_term_config import (
+from mjlab.managers.observation_manager import (
   ObservationGroupCfg,
+  ObservationManager,
   ObservationTermCfg,
-  RewardTermCfg,
 )
-from mjlab.managers.observation_manager import ObservationManager
-from mjlab.managers.reward_manager import RewardManager
+from mjlab.managers.reward_manager import RewardManager, RewardTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
 from mjlab.sim.sim import Simulation, SimulationCfg
 
diff --git a/tests/test_observation_delay.py b/tests/test_observation_delay.py
index f13e3b3c2..87447064c 100644
--- a/tests/test_observation_delay.py
+++ b/tests/test_observation_delay.py
@@ -6,8 +6,11 @@
 import torch
 from conftest import get_test_device
 
-from mjlab.managers.manager_term_config import ObservationGroupCfg, ObservationTermCfg
-from mjlab.managers.observation_manager import ObservationManager
+from mjlab.managers.observation_manager import (
+  ObservationGroupCfg,
+  ObservationManager,
+  ObservationTermCfg,
+)
 
 
 @pytest.fixture
diff --git a/tests/test_observation_history.py b/tests/test_observation_history.py
index d49ba40d3..21b94b9bd 100644
--- a/tests/test_observation_history.py
+++ b/tests/test_observation_history.py
@@ -6,8 +6,11 @@
 import torch
 from conftest import get_test_device
 
-from mjlab.managers.manager_term_config import ObservationGroupCfg, ObservationTermCfg
-from mjlab.managers.observation_manager import ObservationManager
+from mjlab.managers.observation_manager import (
+  ObservationGroupCfg,
+  ObservationManager,
+  ObservationTermCfg,
+)
 
 
 @pytest.fixture
diff --git a/tests/test_rewards.py b/tests/test_rewards.py
index b98ca5c02..f2ad35782 100644
--- a/tests/test_rewards.py
+++ b/tests/test_rewards.py
@@ -10,8 +10,7 @@
 from mjlab.actuator import BuiltinPositionActuatorCfg
 from mjlab.entity import Entity, EntityArticulationInfoCfg, EntityCfg
 from mjlab.envs.mdp.rewards import electrical_power_cost
-from mjlab.managers.manager_term_config import RewardTermCfg
-from mjlab.managers.reward_manager import RewardManager
+from mjlab.managers.reward_manager import RewardManager, RewardTermCfg
 from mjlab.managers.scene_entity_config import SceneEntityCfg
 from mjlab.sim.sim import Simulation, SimulationCfg
 
diff --git a/tests/test_rl_exporter.py b/tests/test_rl_exporter.py
index bb8b2355c..3d6c5d142 100644
--- a/tests/test_rl_exporter.py
+++ b/tests/test_rl_exporter.py
@@ -11,10 +11,7 @@
 from mjlab.actuator import XmlMotorActuatorCfg
 from mjlab.entity import EntityArticulationInfoCfg, EntityCfg
 from mjlab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, mdp
-from mjlab.managers.manager_term_config import (
-  ObservationGroupCfg,
-  ObservationTermCfg,
-)
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
 from mjlab.rl.exporter_utils import (
   attach_metadata_to_onnx,
   get_base_metadata,
diff --git a/tests/test_task_configs.py b/tests/test_task_configs.py
index 7d69e49a2..4582b651f 100644
--- a/tests/test_task_configs.py
+++ b/tests/test_task_configs.py
@@ -3,7 +3,7 @@
 import pytest
 
 from mjlab.envs import ManagerBasedRlEnvCfg
-from mjlab.managers.manager_term_config import ObservationGroupCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg
 from mjlab.tasks.registry import list_tasks, load_env_cfg
 
 
diff --git a/tests/test_terminations.py b/tests/test_terminations.py
index c09bf1ccf..964337eb8 100644
--- a/tests/test_terminations.py
+++ b/tests/test_terminations.py
@@ -8,8 +8,7 @@
 from conftest import get_test_device
 
 from mjlab.envs.mdp.terminations import nan_detection
-from mjlab.managers.manager_term_config import TerminationTermCfg
-from mjlab.managers.termination_manager import TerminationManager
+from mjlab.managers.termination_manager import TerminationManager, TerminationTermCfg
 from mjlab.sim.sim import Simulation, SimulationCfg
 
 
diff --git a/tests/test_tracking_task.py b/tests/test_tracking_task.py
index e81ebda62..656483db9 100644
--- a/tests/test_tracking_task.py
+++ b/tests/test_tracking_task.py
@@ -25,7 +25,6 @@ def test_tracking_tasks_have_motion_command(tracking_task_ids: list[str]) -> Non
   for task_id in tracking_task_ids:
     cfg = load_env_cfg(task_id)
 
-    assert cfg.commands is not None, f"Task {task_id} has no commands"
     assert "motion" in cfg.commands, f"Task {task_id} missing 'motion' command"
 
     motion_cmd = cfg.commands["motion"]
@@ -83,7 +82,6 @@ def test_tracking_play_disables_rsi_randomization() -> None:
   for task_id in tracking_tasks:
     cfg = load_env_cfg(task_id, play=True)
 
-    assert cfg.commands is not None, f"Task {task_id} (play mode) has no commands"
     motion_cmd = cfg.commands["motion"]
     assert isinstance(motion_cmd, MotionCommandCfg), (
       f"Task {task_id} (play mode) motion command is not MotionCommandCfg"
@@ -109,7 +107,6 @@ def test_tracking_play_uses_start_sampling_mode() -> None:
   for task_id in tracking_tasks:
     cfg = load_env_cfg(task_id, play=True)
 
-    assert cfg.commands is not None, f"Task {task_id} (play mode) has no commands"
     motion_cmd = cfg.commands["motion"]
     assert isinstance(motion_cmd, MotionCommandCfg), (
       f"Task {task_id} (play mode) motion command is not MotionCommandCfg"
diff --git a/tests/test_velocity_task.py b/tests/test_velocity_task.py
index da55e2646..9e449e919 100644
--- a/tests/test_velocity_task.py
+++ b/tests/test_velocity_task.py
@@ -43,7 +43,6 @@ def test_velocity_tasks_have_twist_command(velocity_task_ids: list[str]) -> None
   for task_id in velocity_task_ids:
     cfg = load_env_cfg(task_id)
 
-    assert cfg.commands is not None, f"Task {task_id} has no commands"
     assert "twist" in cfg.commands, f"Task {task_id} missing 'twist' command"
 
     twist_cmd = cfg.commands["twist"]
diff --git a/tests/test_xml_actuator.py b/tests/test_xml_actuator.py
index e78888671..e8b90fc0b 100644
--- a/tests/test_xml_actuator.py
+++ b/tests/test_xml_actuator.py
@@ -7,10 +7,7 @@
 from mjlab.actuator import XmlMotorActuatorCfg
 from mjlab.entity import Entity, EntityArticulationInfoCfg, EntityCfg
 from mjlab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, mdp
-from mjlab.managers.manager_term_config import (
-  ObservationGroupCfg,
-  ObservationTermCfg,
-)
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
 from mjlab.scene import SceneCfg
 from mjlab.sim import MujocoCfg, SimulationCfg
 from mjlab.terrains import TerrainImporterCfg