diff --git a/README.md b/README.md
index 0995ca528..52c416494 100644
--- a/README.md
+++ b/README.md
@@ -43,6 +43,12 @@ pip install --upgrade pip
 pip install brax
 ```
 
+You may also install from [Conda](https://docs.conda.io/en/latest/) or [Mamba](https://github.com/mamba-org/mamba):
+
+```
+conda install -c conda-forge brax  # s/conda/mamba for mamba
+```
+
 Alternatively, to install Brax from source, clone this repo, `cd` to it, and then:
 
 ```
diff --git a/brax/experimental/biggym/tasks.py b/brax/experimental/biggym/tasks.py
index 246fd05de..25049ea8c 100644
--- a/brax/experimental/biggym/tasks.py
+++ b/brax/experimental/biggym/tasks.py
@@ -27,8 +27,7 @@ def get_match_env_name(task_name: str, comp1: str, comp2):
   return f'match_{task_name}__{comp1}__{comp2}'
 
 
-def race(component: str, pos: Tuple[float, float, float] = (0, 0, 0),
-         **component_params):
+def race(component: str, pos: Tuple[float, float, float] = (0, 0, 0), **component_params):
   return dict(
       components=dict(
           agent1=dict(
diff --git a/brax/physics/colliders.py b/brax/physics/colliders.py
index 9b1cf5800..04e3e4b01 100644
--- a/brax/physics/colliders.py
+++ b/brax/physics/colliders.py
@@ -999,12 +999,14 @@ def get(config: config_pb2.Config, body: bodies.Body) -> List[Collider]:
         if config.collider_cutoff and len(
             bodies_a) > config.collider_cutoff and (
                 type_a, type_b) in supported_near_neighbors:
+          # pytype: disable=wrong-arg-types
           col_a = cls_a(bodies_a, body)
           col_b = cls_b(bodies_b, body)
           cull = NearNeighbors(
               cls_a(unique_bodies, body), cls_b(unique_bodies, body),
               (col_a.body.idx, col_b.body.idx), config.collider_cutoff)
         else:
+          # pytype: disable=wrong-arg-types
           cull = Pairs(cls_a(bodies_a, body), cls_b(bodies_b, body))
         if b_is_frozen:
           collider = OneWayCollider(contact_fn, cull, config)
diff --git a/brax/training/replay_buffers.py b/brax/training/replay_buffers.py
index c63a7687e..a46bea8ef 100644
--- a/brax/training/replay_buffers.py
+++ b/brax/training/replay_buffers.py
@@ -23,7 +23,6 @@
 from jax import flatten_util
 from jax.experimental import maps
 from jax.experimental import pjit
-from jax.interpreters import pxla
 import jax.numpy as jnp
 
 State = TypeVar('State')
@@ -267,7 +266,6 @@ def psize(buffer_state):
     return jax.pmap(psize, axis_name=axis_name)(buffer_state)[0]
 
 
-# TODO: Make this multi-host and GDS compatible.
 class PjitWrapper(ReplayBuffer[State, Sample]):
   """Wrapper to distribute the buffer on multiple devices with pjit.
 
@@ -284,7 +282,7 @@ def __init__(self,
                buffer: ReplayBuffer[State, Sample],
                mesh: maps.Mesh,
                axis_name: str,
-               batch_partion_spec: Optional[pxla.PartitionSpec] = None):
+               batch_partition_spec: Optional[pjit.PartitionSpec] = None):
     """Constructor.
 
     Args:
@@ -292,7 +290,7 @@ def __init__(self,
       mesh: Device mesh for pjitting context.
       axis_name: The axis along which the replay buffer data should be
         partitionned.
-      batch_partion_spec: PartitionSpec of the inserted/sampled batch.
+      batch_partition_spec: PartitionSpec of the inserted/sampled batch.
     """
     self._buffer = buffer
     self._mesh = mesh
@@ -320,17 +318,17 @@ def sample(buffer_state: State) -> Tuple[State, Sample]:
     def size(buffer_state: State) -> int:
       return jnp.sum(jax.vmap(self._buffer.size)(buffer_state))
 
-    partition_spec = pxla.PartitionSpec((axis_name,))
+    partition_spec = pjit.PartitionSpec((axis_name,))
     self._partitioned_init = pjit.pjit(
         init, in_axis_resources=None, out_axis_resources=partition_spec)
     self._partitioned_insert = pjit.pjit(
         insert,
-        in_axis_resources=(partition_spec, batch_partion_spec),
+        in_axis_resources=(partition_spec, batch_partition_spec),
         out_axis_resources=partition_spec)
     self._partitioned_sample = pjit.pjit(
         sample,
         in_axis_resources=partition_spec,
-        out_axis_resources=(partition_spec, batch_partion_spec))
+        out_axis_resources=(partition_spec, batch_partition_spec))
     # This will return the TOTAL size accross all devices.
     self._partitioned_size = pjit.pjit(
         size, in_axis_resources=partition_spec, out_axis_resources=None)