diff --git a/rllib/tuned_examples/sac/humanoid_sac.py b/rllib/tuned_examples/sac/humanoid_sac.py
new file mode 100644
index 000000000000..2245ee61d5ed
--- /dev/null
+++ b/rllib/tuned_examples/sac/humanoid_sac.py
@@ -0,0 +1,71 @@
+"""This is WIP.
+
+On a single-GPU machine, with the --num-gpus=1 command line option, this example should
+learn a episode return of >1000 in ~10h, which is still very basic, but does somewhat
+prove SAC's capabilities. Some more hyperparameter fine tuning, longer runs, and
+more scale (`--num-gpus > 1` and `--num-env-runners > 0`) should help push this up.
+"""
+
+from torch import nn
+
+from ray.rllib.algorithms.sac.sac import SACConfig
+from ray.rllib.utils.test_utils import add_rllib_example_script_args
+
+parser = add_rllib_example_script_args(
+    default_timesteps=1000000,
+    default_reward=12000.0,
+    default_iters=2000,
+)
+parser.set_defaults(enable_new_api_stack=True)
+# Use `parser` to add your own custom command line options to this script
+# and (if needed) use their values to set up `config` below.
+args = parser.parse_args()
+
+
+config = (
+    SACConfig()
+    .api_stack(
+        enable_rl_module_and_learner=True,
+        enable_env_runner_and_connector_v2=True,
+    )
+    .environment("Humanoid-v4")
+    .training(
+        initial_alpha=1.001,
+        actor_lr=0.00005,
+        critic_lr=0.00005,
+        alpha_lr=0.00005,
+        target_entropy="auto",
+        n_step=(1, 3),
+        tau=0.005,
+        train_batch_size_per_learner=256,
+        target_network_update_freq=1,
+        replay_buffer_config={
+            "type": "PrioritizedEpisodeReplayBuffer",
+            "capacity": 1000000,
+            "alpha": 0.6,
+            "beta": 0.4,
+        },
+        num_steps_sampled_before_learning_starts=10000,
+    )
+    .rl_module(
+        model_config_dict={
+            "fcnet_hiddens": [1024, 1024],
+            "fcnet_activation": "relu",
+            "fcnet_weights_initializer": nn.init.xavier_uniform_,
+            "post_fcnet_hiddens": [],
+            "post_fcnet_activation": None,
+            "post_fcnet_weights_initializer": "orthogonal_",
+            "post_fcnet_weights_initializer_config": {"gain": 0.01},
+        }
+    )
+    .reporting(
+        metrics_num_episodes_for_smoothing=5,
+        min_sample_timesteps_per_iteration=1000,
+    )
+)
+
+
+if __name__ == "__main__":
+    from ray.rllib.utils.test_utils import run_rllib_example_script_experiment
+
+    run_rllib_example_script_experiment(config, args)