diff --git a/map_nav_src/reverie/main_nav_obj.py b/map_nav_src/reverie/main_nav_obj.py
index 641943e..5d63754 100644
--- a/map_nav_src/reverie/main_nav_obj.py
+++ b/map_nav_src/reverie/main_nav_obj.py
@@ -138,7 +138,7 @@ def train(args, train_env, val_envs, aug_env=None, rank=-1):
             '\nListener training starts, start iteration: %s' % str(start_iter), record_file
         )
 
-    best_val = {'val_unseen': {"spl": 0., "sr": 0., "state":"", "sspl": 0., 'found_sr': 0.}}
+    best_val = {'val_unseen': {"spl": 0., "sr": 0., "room_sr": 0., "state":"", "sspl": 0., 'found_sr': 0.}}
 
     for idx in range(start_iter, start_iter+args.iters, args.log_every):
         listner.logs = defaultdict(list)
@@ -203,11 +203,12 @@ def train(args, train_env, val_envs, aug_env=None, rank=-1):
 
                 # select model by spl
                 if env_name in best_val:
-                    if score_summary['sspl'] >= best_val[env_name]['sspl']:
+                    if score_summary['room_sr'] >= best_val[env_name]['room_sr']:
                         best_val[env_name]['spl'] = score_summary['spl']
                         best_val[env_name]['sspl'] = score_summary['sspl']
                         best_val[env_name]['sr'] = score_summary['sr']
                         best_val[env_name]['found_sr'] = score_summary['found_sr']
+                        best_val[env_name]['room_sr'] = score_summary['room_sr']
                         best_val[env_name]['state'] = 'Iter %d %s' % (iter, loss_str)
                         listner.save(idx, os.path.join(args.ckpt_dir, "best_%s" % (env_name)))