diff --git a/brax/training/agents/apg/train.py b/brax/training/agents/apg/train.py
index 0c6ad2fc..c635fbdf 100644
--- a/brax/training/agents/apg/train.py
+++ b/brax/training/agents/apg/train.py
@@ -174,7 +174,6 @@ def loss(policy_params, normalizer_params, env_state, key):
     (state_h, _), (rewards,
      obs) = jax.lax.scan(f, (env_state, key),
                          (jnp.arange(horizon_length // action_repeat)))
-    jax.debug.print("{x}", x=jnp.mean(rewards))
 
     return -jnp.mean(rewards), (obs, state_h)
 
@@ -245,7 +244,7 @@ def training_epoch_with_timing(training_state: TrainingState,
 
     epoch_training_time = time.time() - t
     training_walltime += epoch_training_time
-    sps = (episode_length * num_envs) / epoch_training_time
+    sps = (updates_per_epoch * num_envs * horizon_length) / epoch_training_time
     metrics = {
         'training/sps': sps,
         'training/walltime': training_walltime,
@@ -292,13 +291,13 @@ def training_epoch_with_timing(training_state: TrainingState,
   # Run initial eval
   metrics = {}
 
-  # if process_id == 0 and num_evals > 1:
-  #   metrics = evaluator.run_evaluation(
-  #       _unpmap(
-  #           (training_state.normalizer_params, training_state.policy_params)),
-  #       training_metrics={})
-  #   logging.info(metrics)
-  #   progress_fn(0, metrics)
+  if process_id == 0 and num_evals > 1:
+    metrics = evaluator.run_evaluation(
+        _unpmap(
+            (training_state.normalizer_params, training_state.policy_params)),
+        training_metrics={})
+    logging.info(metrics)
+    progress_fn(0, metrics)
 
   init_key, scramble_key, local_key = jax.random.split(local_key, 3)
   init_key = jax.random.split(init_key, (local_devices_to_use, num_envs // process_count))
@@ -313,29 +312,17 @@ def training_epoch_with_timing(training_state: TrainingState,
   for it in range(num_evals_after_init):
     logging.info('starting iteration %s %s', it, time.time() - xt)
 
-    # optimization
-    from pathlib import Path
-    import pickle
-
-    file_name = f'checkpoint_{it}.pkl'
-    base_path = "/tmp/checkpoints/"
-    save_to = str(Path(
-                    Path(base_path),
-                    Path(file_name)))
-    algo_state = {'training_state': training_state}
-    pickle.dump(algo_state, open(save_to, "wb"))
-
     (training_state, env_state,
      training_metrics, epoch_key) = training_epoch_with_timing(training_state, env_state, epoch_key)
 
-    # if process_id == 0:
-    #   # Run evals.
-    #   metrics = evaluator.run_evaluation(
-    #       _unpmap(
-    #           (training_state.normalizer_params, training_state.policy_params)),
-    #       training_metrics)
-    #   logging.info(metrics)
-    #   progress_fn(it + 1, metrics)
+    if process_id == 0:
+      # Run evals.
+      metrics = evaluator.run_evaluation(
+          _unpmap(
+              (training_state.normalizer_params, training_state.policy_params)),
+          training_metrics)
+      logging.info(metrics)
+      progress_fn(it + 1, metrics)
 
   # If there was no mistakes the training_state should still be identical on all
   # devices.