diff --git a/algorithmic_efficiency/logger_utils.py b/algorithmic_efficiency/logger_utils.py
index af2e61581..2b3cf86f6 100644
--- a/algorithmic_efficiency/logger_utils.py
+++ b/algorithmic_efficiency/logger_utils.py
@@ -275,6 +275,12 @@ def get_meta_data(workload: spec.Workload) -> dict:
   return meta_data
 
 
+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})
+  write_json(meta_file_name, meta_data)
+
+
 class MetricLogger(object):
   """Used to log all measurements during training.
 
diff --git a/setup.cfg b/setup.cfg
index 6f53cd51b..a7ce5ebb2 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -115,6 +115,7 @@ jax_core_deps =
   # Todo(kasimbeg): verify if this is necessary after we
   # upgrade jax.
   chex==0.1.7
+  ml_dtypes==0.2.0
 
 # JAX CPU
 jax_cpu =
diff --git a/submission_runner.py b/submission_runner.py
index f4ee32ede..2289d39d3 100644
--- a/submission_runner.py
+++ b/submission_runner.py
@@ -133,6 +133,11 @@
 flags.DEFINE_boolean('save_checkpoints',
                      True,
                      'Whether or not to checkpoint the model at every eval.')
+flags.DEFINE_integer(
+    'rng_seed',
+    None,
+    'Value of rng seed. If None, a random seed will'
+    'be generated from hardware.')
 FLAGS = flags.FLAGS
 USE_PYTORCH_DDP, RANK, DEVICE, N_GPUS = pytorch_setup()
 
@@ -173,6 +178,7 @@ 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,
@@ -267,10 +273,9 @@ def train_once(
          global_step,
          preemption_count,
          checkpoint_dir=log_dir)
-    meta_data = logger_utils.get_meta_data(workload)
     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.write_json(meta_file_name, meta_data)
+    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())
@@ -449,7 +454,8 @@ def score_submission_on_workload(workload: spec.Workload,
                                  tuning_search_space: Optional[str] = None,
                                  num_tuning_trials: Optional[int] = None,
                                  log_dir: Optional[str] = None,
-                                 save_checkpoints: Optional[bool] = True):
+                                 save_checkpoints: Optional[bool] = True,
+                                 rng_seed: Optional[int] = None):
   # Expand paths because '~' may not be recognized
   data_dir = os.path.expanduser(data_dir)
   if imagenet_v2_data_dir:
@@ -496,7 +502,8 @@ def score_submission_on_workload(workload: spec.Workload,
     all_metrics = []
     for hi, hyperparameters in enumerate(tuning_search_space):
       # Generate a new seed from hardware sources of randomness for each trial.
-      rng_seed = struct.unpack('I', os.urandom(4))[0]
+      if not rng_seed:
+        rng_seed = struct.unpack('I', os.urandom(4))[0]
       logging.info('Using RNG seed %d', rng_seed)
       rng = prng.PRNGKey(rng_seed)
       # Because we initialize the PRNGKey with only a single 32 bit int, in the
@@ -528,7 +535,9 @@ 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,
@@ -545,7 +554,8 @@ def score_submission_on_workload(workload: spec.Workload,
       logging.info(f'Total number of evals: {num_evals}')
       logging.info('=' * 20)
   else:
-    rng_seed = struct.unpack('q', os.urandom(8))[0]
+    if not rng_seed:
+      rng_seed = struct.unpack('q', os.urandom(8))[0]
     rng = prng.PRNGKey(rng_seed)
     # If the submission is responsible for tuning itself, we only need to run it
     # once and return the total time.
@@ -554,7 +564,7 @@ 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
 
@@ -610,7 +620,8 @@ def main(_):
       tuning_search_space=FLAGS.tuning_search_space,
       num_tuning_trials=FLAGS.num_tuning_trials,
       log_dir=logging_dir_path,
-      save_checkpoints=FLAGS.save_checkpoints)
+      save_checkpoints=FLAGS.save_checkpoints,
+      rng_seed=FLAGS.rng_seed)
   logging.info(f'Final {FLAGS.workload} score: {score}')
 
   if FLAGS.profile: