diff --git a/flax/nnx/__init__.py b/flax/nnx/__init__.py
index ac0fd2391..d45433b8e 100644
--- a/flax/nnx/__init__.py
+++ b/flax/nnx/__init__.py
@@ -43,6 +43,7 @@
 from .pytreelib import is_data as is_data
 from .pytreelib import has_data as has_data
 from .pytreelib import check_pytree as check_pytree
+from .pytreelib import prefix
 from .helpers import Dict as Dict
 from .helpers import List as List
 from .helpers import Sequential as Sequential
diff --git a/flax/nnx/pytreelib.py b/flax/nnx/pytreelib.py
index c53a71314..64756f178 100644
--- a/flax/nnx/pytreelib.py
+++ b/flax/nnx/pytreelib.py
@@ -38,6 +38,7 @@
 )
 from flax import config
 from flax.nnx.variablelib import Variable
+from flax.nnx import filterlib
 from flax.typing import MISSING, Missing, SizeBytes
 
 BUILDING_DOCS = 'FLAX_DOC_BUILD' in os.environ
@@ -112,6 +113,18 @@ def __init__(self):
   metadata['static'] = False
   return dataclasses.field(**kwargs, metadata=metadata)  # type: ignore[return-value]
 
+def prefix(pytree, filter_map):
+  preds = [(filterlib.to_predicate(k), v) for k, v in filter_map.items()]
+
+  def lookup(path, value):
+    for (pred,obj) in preds:
+      if pred(graphlib.jax_to_nnx_path(path), value):
+        return obj
+    return None
+
+  return jax.tree.map_with_path(lookup, pytree,
+    is_leaf=lambda p, v: isinstance(v, variablelib.Variable) or lookup(p, v) is not None,
+    is_leaf_takes_path=True)
 
 def register_data_type(type_: T, /) -> T:
   """Registers a type as pytree data type recognized by Object.
@@ -1061,4 +1074,4 @@ def _maybe_int(x):
     return x
 
 def _get_str(x):
-  return x if isinstance(x, str) else str(x)
\ No newline at end of file
+  return x if isinstance(x, str) else str(x)
diff --git a/tests/nnx/rngs_test.py b/tests/nnx/rngs_test.py
index 9f01582bd..9500fc081 100644
--- a/tests/nnx/rngs_test.py
+++ b/tests/nnx/rngs_test.py
@@ -216,6 +216,30 @@ def test_fork_rngs(self, graph):
     nnx.restore_rngs(backups)
     self.assertNotEqual(rngs.params.key, new_key)
 
+  def test_prefix(self):
+    class Model(nnx.Module):
+      def __init__(self, rngs: nnx.Rngs):
+        self.linear = nnx.Linear(20, 10, rngs=rngs)
+        self.drop = nnx.Dropout(0.1, rngs=rngs)
+      def __call__(self, x):
+        return self.drop(self.linear(x))
+
+    with nnx.graphlib.set_graph_updates(False):
+      with nnx.graphlib.set_graph_mode(False):
+        rngs = nnx.Rngs(0, dropout=jax.random.key(1))
+        rngs = rngs.split({f'dropout': 5})
+        prefix = nnx.prefix(rngs, {'dropout': 0})
+        model = nnx.vmap(Model, in_axes=(prefix,))(rngs)
+        assert model.drop.rngs.key[...].shape == (5,)
+        assert model.drop.rngs.count[...].shape == (5,)
+        bias = model.linear.bias[...]
+        assert all(jnp.allclose(x,y) for (x,y) in zip(bias, bias[1:]))
+
+        # This is the same as just using 0 as the model in_axes
+        prefix2 = nnx.prefix(model, {nnx.Variable: 0})
+        nnx.vmap(Model.__call__, in_axes=(prefix2,None))(model, jnp.ones(20))
+
+
   def test_random_helpers(self):
     rngs = nnx.Rngs(0, params=1)