diff --git a/src/gfn/containers/replay_buffer.py b/src/gfn/containers/replay_buffer.py
index 2bc870ca..fe1b1110 100644
--- a/src/gfn/containers/replay_buffer.py
+++ b/src/gfn/containers/replay_buffer.py
@@ -167,7 +167,6 @@ def add(self, training_objects: Transitions | Trajectories | tuple[States]):
             training_objects, terminating_states = training_objects
 
         to_add = len(training_objects)
-
         self._is_full |= len(self) + to_add >= self.capacity
 
         # The buffer isn't full yet.
@@ -177,18 +176,14 @@ def add(self, training_objects: Transitions | Trajectories | tuple[States]):
         # Our buffer is full and we will prioritize diverse, high reward additions.
         else:
             # Sort the incoming elements by their logrewards.
-            ix = torch.argsort(training_objects._log_rewards, descending=True)
+            ix = torch.argsort(training_objects.log_rewards, descending=True)
             training_objects = training_objects[ix]
 
             # Filter all batch logrewards lower than the smallest logreward in buffer.
             min_reward_in_buffer = self.training_objects.log_rewards.min()
-            idx_bigger_rewards = training_objects.log_rewards > min_reward_in_buffer
+            idx_bigger_rewards = training_objects.log_rewards >= min_reward_in_buffer
             training_objects = training_objects[idx_bigger_rewards]
 
-            # Compute all pairwise distances between the batch and the buffer.
-            curr_dim = training_objects.last_states.batch_shape[0]
-            buffer_dim = self.training_objects.last_states.batch_shape[0]
-
             # TODO: Concatenate input with final state for conditional GFN.
             # if self.is_conditional:
             #     batch = torch.cat(
@@ -199,37 +194,41 @@ def add(self, training_objects: Transitions | Trajectories | tuple[States]):
             #         [self.storage["input"], self.storage["final_state"]],
             #         dim=-1,
             #     )
-            batch = training_objects.last_states.tensor.float()
-            buffer = self.training_objects.last_states.tensor.float()
 
             # Filter the batch for diverse final_states with high reward.
+            batch = training_objects.last_states.tensor.float()
+            batch_dim = training_objects.last_states.batch_shape[0]
             batch_batch_dist = torch.cdist(
-                batch.view(curr_dim, -1).unsqueeze(0),
-                batch.view(curr_dim, -1).unsqueeze(0),
+                batch.view(batch_dim, -1).unsqueeze(0),
+                batch.view(batch_dim, -1).unsqueeze(0),
                 p=self.p_norm_distance,
             ).squeeze(0)
 
+            # Finds the min distance at each row, and removes rows below the cutoff.
             r, w = torch.triu_indices(*batch_batch_dist.shape)  # Remove upper diag.
             batch_batch_dist[r, w] = torch.finfo(batch_batch_dist.dtype).max
             batch_batch_dist = batch_batch_dist.min(-1)[0]
+            idx_batch_batch = batch_batch_dist > self.cutoff_distance
+            training_objects = training_objects[idx_batch_batch]
 
-            # Filter the batch for diverse final_states w.r.t the buffer.
+            # Compute all pairwise distances between the remaining batch and the buffer.
+            batch = training_objects.last_states.tensor.float()
+            buffer = self.training_objects.last_states.tensor.float()
+            batch_dim = training_objects.last_states.batch_shape[0]
+            buffer_dim = self.training_objects.last_states.batch_shape[0]
             batch_buffer_dist = (
                 torch.cdist(
-                    batch.view(curr_dim, -1).unsqueeze(0),
+                    batch.view(batch_dim, -1).unsqueeze(0),
                     buffer.view(buffer_dim, -1).unsqueeze(0),
                     p=self.p_norm_distance,
                 )
                 .squeeze(0)
-                .min(-1)[0]
+                .min(-1)[0]  # Min calculated over rows, i.e., over the batch elements.
             )
 
-            # Remove non-diverse examples according to the above distances.
-            idx_batch_batch = batch_batch_dist > self.cutoff_distance
+            # Filter the batch for diverse final_states w.r.t the buffer.
             idx_batch_buffer = batch_buffer_dist > self.cutoff_distance
-            idx_diverse = idx_batch_batch & idx_batch_buffer
-
-            training_objects = training_objects[idx_diverse]
+            training_objects = training_objects[idx_batch_buffer]
 
             # If any training object remain after filtering, add them.
             if len(training_objects):