diff --git a/codes/models/steps/losses.py b/codes/models/steps/losses.py
index d70a2ee6..c5419a78 100644
--- a/codes/models/steps/losses.py
+++ b/codes/models/steps/losses.py
@@ -392,6 +392,10 @@ class RecurrentLoss(ConfigurableLoss):
         o['fake'] = '_fake'
         o['real'] = '_real'
         self.loss = create_loss(o, self.env)
+        # Use this option to specify a differential weighting scheme for losses inside of the recurrent construct. For
+        # example, if later recurrent outputs should contribute more to the loss than earlier ones. When specified,
+        # must be a list of weights that exactly aligns with the recurrent list fed to forward().
+        self.recurrent_weights = opt['recurrent_weights'] if 'recurrent_weights' in opt.keys() else 1
 
     def forward(self, net, state):
         total_loss = 0
@@ -400,7 +404,10 @@ class RecurrentLoss(ConfigurableLoss):
         for i in range(real.shape[1]):
             st['_real'] = real[:, i]
             st['_fake'] = state[self.opt['fake']][:, i]
-            total_loss += self.loss(net, st)
+            subloss = self.loss(net, st)
+            if isinstance(self.recurrent_weights, list);
+                subloss = subloss * self.recurrent_weights[i]
+            total_loss += subloss
         return total_loss
 
     def extra_metrics(self):