diff --git a/swirl_dynamics/projects/debiasing/rectified_flow/models.py b/swirl_dynamics/projects/debiasing/rectified_flow/models.py
index 9dccf18..a2a43be 100644
--- a/swirl_dynamics/projects/debiasing/rectified_flow/models.py
+++ b/swirl_dynamics/projects/debiasing/rectified_flow/models.py
@@ -192,18 +192,18 @@ def eval_fn(
 
     vmap_mult = jax.vmap(jnp.multiply, in_axes=(0, 0))
     x_t = vmap_mult(x_1, time_eval) + vmap_mult(x_0, 1 - time_eval)
-    flow_fn = self.flow_fn(variables, self.flow_model)
+    flow_fn = self.inference_fn(variables, self.flow_model)
     v_t = jax.vmap(flow_fn, in_axes=(1, None), out_axes=1)(
         x_t, time_eval
     )
 
     # Eq. (1) in [1]
     int_losses = jax.vmap(jnp.mean)(jnp.square((x_1 - x_0 - v_t)))
-    eval_losses = {f"sigma_lvl{i}": loss for i, loss in enumerate(int_losses)}
+    eval_losses = {f"time_lvl{i}": loss for i, loss in enumerate(int_losses)}
     return eval_losses
 
   @staticmethod
-  def flow_fn(variables: models.PyTree, flow_model: nn.Module):
+  def inference_fn(variables: models.PyTree, flow_model: nn.Module):
     """Returns the inference flow function."""
 
     def _flow(
diff --git a/swirl_dynamics/projects/debiasing/rectified_flow/trainers.py b/swirl_dynamics/projects/debiasing/rectified_flow/trainers.py
new file mode 100644
index 0000000..0cc9eff
--- /dev/null
+++ b/swirl_dynamics/projects/debiasing/rectified_flow/trainers.py
@@ -0,0 +1,104 @@
+# Copyright 2023 The swirl_dynamics Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Trainers for ReFlow models."""
+
+from collections.abc import Callable
+
+from clu import metrics as clu_metrics
+import flax
+import jax
+import optax
+from swirl_dynamics.projects.debiasing.rectified_flow import models
+from swirl_dynamics.templates import train_states
+from swirl_dynamics.templates import trainers
+
+Array = jax.Array
+VariableDict = trainers.VariableDict
+TrainState = train_states.BasicTrainState
+
+
+class ReFlowTrainer(
+    trainers.BasicTrainer[models.ReFlowModel, TrainState]
+):
+  """Single-device trainer for rectified flow models."""
+
+  @flax.struct.dataclass
+  class TrainMetrics(clu_metrics.Collection):
+    train_loss: clu_metrics.Average.from_output("loss")
+    train_loss_std: clu_metrics.Std.from_output("loss")
+
+  EvalMetrics = clu_metrics.Collection.create(  # pylint: disable=invalid-name
+      **{
+          f"eval_time_lvl{i}": clu_metrics.Average.from_output(
+              f"time_lvl{i}"
+          )
+          for i in range(models.ReFlowModel.num_eval_time_levels)
+      }
+  )
+
+  def initialize_train_state(self, rng: Array) -> TrainState:
+    init_vars = self.model.initialize(rng)
+    mutables, params = flax.core.pop(init_vars, "params")
+    return TrainState.create(
+        replicate=self.is_distributed,
+        params=params,
+        opt_state=self.optimizer.init(params),
+        flax_mutables=mutables,
+    )
+
+  @property
+  def update_train_state(
+      self,
+  ) -> Callable[[TrainState, VariableDict, VariableDict], TrainState]:
+    """Returns function that updates the train state."""
+
+    def _update_train_state(
+        train_state: TrainState,
+        grads: VariableDict,
+        mutables: VariableDict,
+    ) -> TrainState:
+      updates, new_opt_state = self.optimizer.update(
+          grads, train_state.opt_state, train_state.params
+      )
+      new_params = optax.apply_updates(train_state.params, updates)
+
+      return train_state.replace(
+          step=train_state.step + 1,
+          opt_state=new_opt_state,
+          params=new_params,
+          flax_mutables=mutables,
+      )
+
+    return _update_train_state
+
+  @staticmethod
+  def inference_fn_from_state_dict(
+      state: TrainState, *args, **kwargs
+  ):
+    return models.ReFlowModel.inference_fn(
+        state.model_variables, *args, **kwargs
+    )
+
+
+class DistributedReFlowTrainer(
+    ReFlowTrainer,
+    trainers.BasicDistributedTrainer[models.ReFlowModel, TrainState],
+):
+  """Multi-device trainer for rectified flow models."""
+
+  # TODO(lzepedanunez): Write a test for this trainer.
+
+  # MRO: ReFlowTrainer > BasicDistributedTrainer > BasicTrainer
+  ...