@@ -278,6 +278,45 @@ reliability of your simulations over time.
 .. which parameters most significantly impact your simulation results.
 
 
+Seeding a rocket's components
+-----------------------------
+
+A ``StochasticRocket`` holds nested stochastic objects: the motors, the
+aerodynamic surfaces, the rail buttons, the parachutes and the air brakes. Each
+time the rocket is reset, every component attached to it at that moment is given
+a stream of its own, spawned from the seed the reset was given. A main and a
+drogue parachute built from the same ``cd_s`` and ``lag`` are then no longer
+made to consume the same draws as each other. Independent streams can still land
+on equal values, and a specification with no spread always will.
+
+The reset is what builds the tree, not ``add_parachute`` or ``add_nose``. A
+rocket resets itself once while being constructed, when it has no components
+yet, so anything attached afterwards keeps the generator it was built with until
+something resets it again. A serial ``MonteCarlo`` run does not reset it at
+all. A parallel one tries to, once per worker, but hands the model a
+``SeedSequence`` where an integer is wanted, so that path does not get as far
+as building the tree either. Resetting per simulation, from an integer seed the
+caller chooses, is what the Monte Carlo seeding work adds.
+
+Each collection is spawned separately, so adding an aerodynamic surface does not
+move what the parachutes draw. Within a collection the stream follows insertion
+order, so adding a component ahead of another does change what the later one
+draws under a fixed seed. The rocket's own inputs, such as ``mass`` and
+``radius``, use the seed exactly as given.
+
+.. note::
+    A component's *position* is a property of the rocket rather than of the
+    component, so it is drawn from the rocket's own stream.
+
+.. note::
+    A stream belongs to one stochastic wrapper. Storing the same wrapper twice,
+    or sharing one between two rockets, is not supported: the second reset
+    replaces the first, and the two entries end up drawing from one generator.
+
+    A shared ``CustomSampler.seed_group`` keeps its own rule: a group belongs to
+    one model. Sharing one between two components leaves each of them seeding it
+    from their own child, and the last one to be reset decides what both draw.
+
 Conclusion
 ----------
 
 
@@ -2,6 +2,7 @@
 
 import warnings
 
+
 from rocketpy.control import _Controller
 from rocketpy.mathutils.vector_matrix import Vector
 from rocketpy.motors.empty_motor import EmptyMotor
@@ -21,6 +22,7 @@
 from rocketpy.rocket.rocket import Rocket
 from rocketpy.stochastic.stochastic_generic_motor import StochasticGenericMotor
 from rocketpy.stochastic.stochastic_motor_model import StochasticMotorModel
+from rocketpy.tools import _seed_sequence_from, _seed_sequence_to_int
 
 from .stochastic_aero_surfaces import (
     StochasticAirBrakes,
@@ -173,25 +175,45 @@ def __init__(
             coordinate_system_orientation=None,
         )
 
+    # Nested stochastic objects, in spawn order. A collection's stream is
+    # addressed by where its name falls in the two tuples read end to end, so
+    # appending to the first one moves every name in the second.
+    _POSITIONED_COLLECTIONS = ("aerodynamic_surfaces", "motors", "rail_buttons")
+    _PLAIN_COLLECTIONS = ("parachutes", "air_brakes")
+
+    @classmethod
+    def _stochastic_collections(cls):
+        """The names of every attribute holding nested stochastic objects."""
+        return cls._POSITIONED_COLLECTIONS + cls._PLAIN_COLLECTIONS
+
     def _set_stochastic(self, seed=None):
         """Set the stochastic attributes for Components, positions and
         inputs.
 
+        Each component takes its own child of its collection's root, so
+        components stay independent and one seed still reproduces the rocket.
+        The body keeps the seed as given, and a collection has a root of its
+        own, so adding a fin does not move every parachute.
+
         Parameters
         ----------
         seed : int, optional
             Seed for the random number generator.
         """
         super()._set_stochastic(seed)
-        self.aerodynamic_surfaces = self.__reset_components(
-            self.aerodynamic_surfaces, seed
-        )
-        self.motors = self.__reset_components(self.motors, seed)
-        self.rail_buttons = self.__reset_components(self.rail_buttons, seed)
-        for parachute in self.parachutes:
-            parachute._set_stochastic(seed)
+        names = self._stochastic_collections()
+        roots = dict(zip(names, _seed_sequence_from(seed).spawn(len(names))))
+        for name in self._POSITIONED_COLLECTIONS:
+            setattr(
+                self, name, self.__reset_components(getattr(self, name), roots[name])
+            )
+        for name in self._PLAIN_COLLECTIONS:
+            for component in getattr(self, name):
+                component._set_stochastic(
+                    _seed_sequence_to_int(roots[name].spawn(1)[0])
+                )
 
-    def __reset_components(self, components, seed):
+    def __reset_components(self, components, root):
         """Creates a new Components whose stochastic structures
         and their positions are reset.
 
@@ -200,8 +222,8 @@ def __reset_components(self, components, seed):
         components : Components
             The components which contains the stochastic structure that
             will be used to create the new components.
-        seed : int, optional
-            Seed for the random number generator.
+        root : numpy.random.SeedSequence
+            The reseed's root. Each component takes its own spawned child.
 
         Returns
         -------
@@ -213,7 +235,7 @@ def __reset_components(self, components, seed):
         new_components = Components()
         for stochastic_obj, _ in components:
             stochastic_obj_position_info = self.__components_map[stochastic_obj]
-            stochastic_obj._set_stochastic(seed)
+            stochastic_obj._set_stochastic(_seed_sequence_to_int(root.spawn(1)[0]))
             new_components.add(
                 stochastic_obj,
                 self._validate_position(stochastic_obj, stochastic_obj_position_info),
 
@@ -1479,6 +1479,18 @@ def find_obj_from_hash(obj, hash_, depth_limit=None):
     return None
 
 
+def _seed_sequence_from(seed):
+    """Returns a ``SeedSequence`` of the caller's own to spawn from.
+
+    A parallel run is handed one that ``SeedSequence`` will not take as
+    entropy, and spawning from it directly would advance the counter of an
+    object the caller still holds, so it is copied from its full state.
+    """
+    if isinstance(seed, np.random.SeedSequence):
+        return np.random.SeedSequence(**seed.state)
+    return np.random.SeedSequence(seed)
+
+
 if __name__ == "__main__":  # pragma: no cover
     import doctest