diff --git a/compiler_opt/es/blackbox_learner.py b/compiler_opt/es/blackbox_learner.py
index 79c61acc..ab67ae4e 100644
--- a/compiler_opt/es/blackbox_learner.py
+++ b/compiler_opt/es/blackbox_learner.py
@@ -35,6 +35,9 @@
 # If less than 40% of requests succeed, skip the step.
 _SKIP_STEP_SUCCESS_RATIO = 0.4
 
+# The percentiles to report as individual values in Tensorboard.
+_PERCENTILES_TO_REPORT = [25, 50, 75]
+
 
 @gin.configurable
 @dataclasses.dataclass(frozen=True)
@@ -188,6 +191,16 @@ def _log_tf_summary(self, rewards: List[float]) -> None:
       tf.summary.scalar(
           'reward/average_reward_train', np.mean(rewards), step=self._step)
 
+      tf.summary.scalar(
+          'reward/maximum_reward_train', np.max(rewards), step=self._step)
+
+      for percentile_to_report in _PERCENTILES_TO_REPORT:
+        percentile_value = np.percentile(rewards, percentile_to_report)
+        tf.summary.scalar(
+            f'reward/{percentile_value}_percentile',
+            percentile_value,
+            step=self._step)
+
       tf.summary.histogram('reward/reward_train', rewards, step=self._step)
 
       train_regressions = [reward for reward in rewards if reward < 0]