diff --git a/docs/tutorials/3_train_gail.ipynb b/docs/tutorials/3_train_gail.ipynb
index 11a6f3e94..f729aa002 100644
--- a/docs/tutorials/3_train_gail.ipynb
+++ b/docs/tutorials/3_train_gail.ipynb
@@ -89,7 +89,7 @@
    "outputs": [],
    "source": [
     "from imitation.algorithms.adversarial.gail import GAIL\n",
-    "from imitation.rewards.reward_nets import BasicShapedRewardNet\n",
+    "from imitation.rewards.reward_nets import BasicRewardNet\n",
     "from imitation.util.networks import RunningNorm\n",
     "from stable_baselines3 import PPO\n",
     "from stable_baselines3.ppo import MlpPolicy\n",
@@ -100,11 +100,12 @@
     "    policy=MlpPolicy,\n",
     "    batch_size=64,\n",
     "    ent_coef=0.0,\n",
-    "    learning_rate=0.00001,\n",
-    "    n_epochs=1,\n",
+    "    learning_rate=0.0004,\n",
+    "    gamma=0.95,\n",
+    "    n_epochs=5,\n",
     "    seed=SEED,\n",
     ")\n",
-    "reward_net = BasicShapedRewardNet(\n",
+    "reward_net = BasicRewardNet(\n",
     "    observation_space=env.observation_space,\n",
     "    action_space=env.action_space,\n",
     "    normalize_input_layer=RunningNorm,\n",
@@ -112,8 +113,8 @@
     "gail_trainer = GAIL(\n",
     "    demonstrations=rollouts,\n",
     "    demo_batch_size=1024,\n",
-    "    gen_replay_buffer_capacity=2048,\n",
-    "    n_disc_updates_per_round=4,\n",
+    "    gen_replay_buffer_capacity=512,\n",
+    "    n_disc_updates_per_round=8,\n",
     "    venv=env,\n",
     "    gen_algo=learner,\n",
     "    reward_net=reward_net,\n",
@@ -126,7 +127,7 @@
     ")\n",
     "\n",
     "# train the learner and evaluate again\n",
-    "gail_trainer.train(20000)\n",
+    "gail_trainer.train(800_000)\n",
     "env.seed(SEED)\n",
     "learner_rewards_after_training, _ = evaluate_policy(\n",
     "    learner, env, 100, return_episode_rewards=True\n",
@@ -137,7 +138,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When we look at the histograms of rewards before and after learning, we can see that the learner is not perfect yet, but it made some progress at least."
+    "We can see that an untrained policy performs poorly, while GAIL matches expert returns (500):"
    ]
   },
   {
@@ -146,17 +147,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "print(\"mean reward after training:\", np.mean(learner_rewards_after_training))\n",
-    "print(\"mean reward before training:\", np.mean(learner_rewards_before_training))\n",
-    "\n",
-    "plt.hist(\n",
-    "    [learner_rewards_before_training, learner_rewards_after_training],\n",
-    "    label=[\"untrained\", \"trained\"],\n",
-    ")\n",
-    "plt.legend()\n",
-    "plt.show()"
+    "print(\"Mean reward before training:\", np.mean(learner_rewards_before_training))\n",
+    "print(\"Mean reward after training:\", np.mean(learner_rewards_after_training))"
    ]
   }
  ],
diff --git a/docs/tutorials/4_train_airl.ipynb b/docs/tutorials/4_train_airl.ipynb
index e7bb6bb99..b2f45a327 100644
--- a/docs/tutorials/4_train_airl.ipynb
+++ b/docs/tutorials/4_train_airl.ipynb
@@ -31,6 +31,13 @@
     "\n",
     "SEED = 42\n",
     "\n",
+    "FAST = True\n",
+    "\n",
+    "if FAST:\n",
+    "    N_RL_TRAIN_STEPS = 800_000\n",
+    "else:\n",
+    "    N_RL_TRAIN_STEPS = 2_000_000\n",
+    "\n",
     "env = make_vec_env(\n",
     "    \"seals/CartPole-v0\",\n",
     "    rng=np.random.default_rng(SEED),\n",
@@ -96,10 +103,13 @@
     "learner = PPO(\n",
     "    env=env,\n",
     "    policy=MlpPolicy,\n",
-    "    batch_size=16,\n",
+    "    batch_size=64,\n",
     "    ent_coef=0.0,\n",
-    "    learning_rate=0.0001,\n",
-    "    n_epochs=2,\n",
+    "    learning_rate=0.0005,\n",
+    "    gamma=0.95,\n",
+    "    clip_range=0.1,\n",
+    "    vf_coef=0.1,\n",
+    "    n_epochs=5,\n",
     "    seed=SEED,\n",
     ")\n",
     "reward_net = BasicShapedRewardNet(\n",
@@ -109,9 +119,9 @@
     ")\n",
     "airl_trainer = AIRL(\n",
     "    demonstrations=rollouts,\n",
-    "    demo_batch_size=1024,\n",
-    "    gen_replay_buffer_capacity=2048,\n",
-    "    n_disc_updates_per_round=4,\n",
+    "    demo_batch_size=2048,\n",
+    "    gen_replay_buffer_capacity=512,\n",
+    "    n_disc_updates_per_round=16,\n",
     "    venv=env,\n",
     "    gen_algo=learner,\n",
     "    reward_net=reward_net,\n",
@@ -121,7 +131,7 @@
     "learner_rewards_before_training, _ = evaluate_policy(\n",
     "    learner, env, 100, return_episode_rewards=True\n",
     ")\n",
-    "airl_trainer.train(20000)\n",
+    "airl_trainer.train(N_RL_TRAIN_STEPS)\n",
     "env.seed(SEED)\n",
     "learner_rewards_after_training, _ = evaluate_policy(\n",
     "    learner, env, 100, return_episode_rewards=True\n",
@@ -132,7 +142,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When we look at the histograms of rewards before and after learning, we can see that the learner is not perfect yet, but it made some progress at least."
+    "We can see that an untrained policy performs poorly, while AIRL brings an improvement. To make it match the expert performance (500), set the flag `FAST` to `False` in the first cell."
    ]
   },
   {
@@ -141,17 +151,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "print(\"mean reward after training:\", np.mean(learner_rewards_after_training))\n",
-    "print(\"mean reward before training:\", np.mean(learner_rewards_before_training))\n",
-    "\n",
-    "plt.hist(\n",
-    "    [learner_rewards_before_training, learner_rewards_after_training],\n",
-    "    label=[\"untrained\", \"trained\"],\n",
-    ")\n",
-    "plt.legend()\n",
-    "plt.show()"
+    "print(\"Mean reward before training:\", np.mean(learner_rewards_before_training))\n",
+    "print(\"Mean reward after training:\", np.mean(learner_rewards_after_training))"
    ]
   }
  ],