diff --git a/codes/models/ExtensibleTrainer.py b/codes/models/ExtensibleTrainer.py
index 32b627d3..0ef0afd5 100644
--- a/codes/models/ExtensibleTrainer.py
+++ b/codes/models/ExtensibleTrainer.py
@@ -187,6 +187,14 @@ class ExtensibleTrainer(BaseModel):
             # Steps can opt out of early (or late) training, make sure that happens here.
             if 'after' in s.step_opt.keys() and step < s.step_opt['after'] or 'before' in s.step_opt.keys() and step > s.step_opt['before']:
                 continue
+            # Steps can choose to not execute if a state key is missing.
+            if 'requires' in s.step_opt.keys():
+                requirements_met = True
+                for requirement in s.step_opt['requires']:
+                    if requirement not in state.keys():
+                        requirements_met = False
+                if not requirements_met:
+                    continue
 
             # Only set requires_grad=True for the network being trained.
             nets_to_train = s.get_networks_trained()
diff --git a/codes/models/steps/losses.py b/codes/models/steps/losses.py
index c5419a78..9f0093e4 100644
--- a/codes/models/steps/losses.py
+++ b/codes/models/steps/losses.py
@@ -405,7 +405,7 @@ class RecurrentLoss(ConfigurableLoss):
             st['_real'] = real[:, i]
             st['_fake'] = state[self.opt['fake']][:, i]
             subloss = self.loss(net, st)
-            if isinstance(self.recurrent_weights, list);
+            if isinstance(self.recurrent_weights, list):
                 subloss = subloss * self.recurrent_weights[i]
             total_loss += subloss
         return total_loss