diff --git a/algorithmic_efficiency/logger_utils.py b/algorithmic_efficiency/logger_utils.py
index dcc8754a9..559859515 100644
--- a/algorithmic_efficiency/logger_utils.py
+++ b/algorithmic_efficiency/logger_utils.py
@@ -279,7 +279,7 @@ def save_meta_data(workload: spec.Workload,
                    rng_seed: int, 
                    meta_file_name: str):
   meta_data = get_meta_data(workload)
-  # meta_data.update({'rng_seed': rng_seed})
+  meta_data.update({'rng_seed': rng_seed})
   write_json(meta_file_name, meta_data)
 
 class MetricLogger(object):
diff --git a/submission_runner.py b/submission_runner.py
index 8f55cd882..fb8df198c 100644
--- a/submission_runner.py
+++ b/submission_runner.py
@@ -177,6 +177,7 @@ def train_once(
     update_params: spec.UpdateParamsFn,
     data_selection: spec.DataSelectionFn,
     hyperparameters: Optional[spec.Hyperparameters],
+    rng_seed: int, 
     rng: spec.RandomState,
     profiler: Profiler,
     max_global_steps: int = None,
@@ -273,7 +274,7 @@ def train_once(
          checkpoint_dir=log_dir)
     meta_file_name = os.path.join(log_dir, f'meta_data_{preemption_count}.json')
     logging.info(f'Saving meta data to {meta_file_name}.')
-    logger_utils.save_meta_data(workload, rng, preemption_count)
+    logger_utils.save_meta_data(workload, rng_seed, preemption_count)
     flag_file_name = os.path.join(log_dir, f'flags_{preemption_count}.json')
     logging.info(f'Saving flags to {flag_file_name}.')
     logger_utils.write_json(flag_file_name, flags.FLAGS.flag_values_dict())
@@ -533,7 +534,9 @@ def score_submission_on_workload(workload: spec.Workload,
                                      data_dir, imagenet_v2_data_dir,
                                      init_optimizer_state,
                                      update_params, data_selection,
-                                     hyperparameters, rng,
+                                     hyperparameters, 
+                                     rng_seed,
+                                     rng,
                                      profiler,
                                      max_global_steps,
                                      tuning_dir_name,
@@ -559,7 +562,7 @@ def score_submission_on_workload(workload: spec.Workload,
           workload, global_batch_size, global_eval_batch_size,
           data_dir, imagenet_v2_data_dir,
           init_optimizer_state, update_params, data_selection,
-          None, rng, profiler, max_global_steps, log_dir,
+          None, rng_seed, rng, profiler, max_global_steps, log_dir,
           save_checkpoints=save_checkpoints)
   return score