diff --git a/rocket_learn/ppo.py b/rocket_learn/ppo.py
index cad2d24..be634e6 100644
--- a/rocket_learn/ppo.py
+++ b/rocket_learn/ppo.py
@@ -171,7 +171,7 @@ def _iter():
             iteration += 1
 
             if save_dir:
-                self.save(os.path.join(save_dir, self.logger.project + "_" + "latest"), -1, save_jit)
+                self.save(os.path.join(save_dir, self.logger.project + "_" + "latest"), iteration, save_actor_jit=save_jit, is_latest=True)
                 if iteration % iterations_per_save == 0:
                     self.save(current_run_dir, iteration, save_jit)  # noqa
 
@@ -485,18 +485,19 @@ def load(self, load_location, continue_iterations=True):
             self.total_steps = checkpoint["total_steps"]
             print("Continuing training at iteration " + str(self.starting_iteration))
 
-    def save(self, save_location, current_step, save_actor_jit=False):
+    def save(self, save_location, current_step, save_actor_jit=False, is_latest=False,):
         """
         Save the model weights, optimizer values, and metadata
         :param save_location: where to save
         :param current_step: the current iteration when saved. Use to later continue training
         :param save_actor_jit: save the policy network as a torch jit file for rlbot use
+        :param is_latest: if this file is the "latest" checkpoint, used to decide if real checkpoint number should be used in filename
         """
 
-        version_str = str(self.logger.project) + "_" + str(current_step)
+        version_str = str(self.logger.project) + "_" + (str(current_step) if not is_latest else "c")
         version_dir = save_location + "\\" + version_str
 
-        os.makedirs(version_dir, exist_ok=current_step == -1)
+        os.makedirs(version_dir, exist_ok=is_latest)
 
         torch.save({
             'epoch': current_step,