diff --git a/docs/source/notebooks/sampling_paths.ipynb b/docs/source/notebooks/sampling_paths.ipynb
index 40313937..00302e9a 100644
--- a/docs/source/notebooks/sampling_paths.ipynb
+++ b/docs/source/notebooks/sampling_paths.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e90f3c19",
+   "id": "0",
    "metadata": {
     "tags": [
      "differt-install-preamble",
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bb86860-9fe6-4551-b47a-c61eecd80bed",
+   "id": "1",
    "metadata": {},
    "source": [
     "# Sampling Path Candidates with Machine Learning\n",
@@ -57,8 +57,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "81919e95-6b4e-4816-bd93-5952712b9f56",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9abdb5b-cbdd-4054-acbb-c3f96e6b4b57",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Generating Training Data\n",
@@ -95,8 +95,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0067a997-70e0-4793-8509-7f60d5e8bce7",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,8 +105,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "dc0dd6a1-17e8-46ee-a665-b43c9251bfb7",
+   "execution_count": null,
+   "id": "5",
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +746,6 @@
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           87
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          "x": [
           -62.10765075683594,
@@ -1849,9 +1848,9 @@
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       },
       "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('2884d984-d909-4cb4-80d3-2cf4393b7e4d');\n",
+       "var gd = document.getElementById('956f8e6e-b213-42aa-b12b-867b94a8dd92');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -1894,14 +1893,14 @@
     "    file = get_sionna_scene(\"simple_street_canyon\")\n",
     "    base_scene = TriangleScene.load_xml(file)\n",
     "\n",
-    "base_scene = base_scene.set_assume_quads(False)\n",
+    "base_scene = base_scene.set_assume_quads(True)\n",
     "# Set the above to 'True' of you want to sample quadrilaterals instead of triangles\n",
-    "base_scene.plot(opacity=0.5)"
+    "base_scene.plot()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4e0c417a-2b54-461f-88f6-9923ced6f8ce",
+   "id": "6",
    "metadata": {},
    "source": [
     "From the `base_scene` object, we can generate random variations of it.\n",
@@ -1925,8 +1924,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "e92699b3-aa6f-4c3c-9a7b-a491cb9d0f20",
+   "execution_count": null,
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1953,9 +1952,10 @@
     "    scene = base_scene\n",
     "    bounding_box = scene.mesh.bounding_box\n",
     "    min_z, max_z = bounding_box[:, 2]\n",
-    "    avg_z = 0.5 * (min_z + max_z)\n",
-    "    rx_bounding_box = bounding_box.at[:, 2].set([min_z + 1.5, avg_z])\n",
-    "    tx_bounding_box = bounding_box.at[:, 2].set([avg_z + 5, max_z + 5])\n",
+    "    rx_bounding_box = bounding_box.at[:, 2].set([min_z + 0.5, min_z + 2.0])\n",
+    "    rx_bounding_box = bounding_box.at[:, 1].set([-5.0, +5.0])\n",
+    "    tx_bounding_box = bounding_box.at[:, 2].set([max_z + 5.0, max_z + -5.0])\n",
+    "    tx_bounding_box = bounding_box.at[:, 1].set([-5.0, +5.0])\n",
     "    key_tx, key_rx = jax.random.split(key, 2)\n",
     "    scene = eqx.tree_at(\n",
     "        lambda s: s.transmitters,\n",
@@ -1990,7 +1990,9 @@
     "    key_tx_rx, key_num_objects, key_sample_triangles = jax.random.split(key, 3)\n",
     "    scene = random_tx_rx(scene, key=key_tx_rx)\n",
     "    num_objects = scene.mesh.num_objects\n",
-    "    num_objects = jax.random.randint(key_num_objects, (), 1, num_objects + 1)\n",
+    "    num_objects = jax.random.randint(\n",
+    "        key_num_objects, (), num_objects // 2, num_objects + 1\n",
+    "    )\n",
     "    return eqx.tree_at(\n",
     "        lambda s: s.mesh,\n",
     "        scene,\n",
@@ -2030,7 +2032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "158e7f0f-6443-4f32-ba8f-d4e4c3c83c25",
+   "id": "8",
    "metadata": {},
    "source": [
     "Below, we demonstrate how to generate (and plot) a random scene."
@@ -2038,8 +2040,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "4f45855c-0e2e-4cba-9d50-e6f9504d8190",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
    "outputs": [
     {
@@ -2047,8 +2049,8 @@
      "output_type": "stream",
      "text": [
       "(order = 0) Found  1 valid paths our of    1 path candidates.\n",
-      "(order = 1) Found  0 valid paths our of   15 path candidates.\n",
-      "(order = 2) Found  0 valid paths our of  210 path candidates.\n"
+      "(order = 1) Found  1 valid paths our of   24 path candidates.\n",
+      "(order = 2) Found  1 valid paths our of  552 path candidates.\n"
      ]
     },
     {
@@ -2067,10 +2069,10 @@
           -70.398193359375
          ],
          "y": [
-          11.168071746826172
+          0.9022369384765624
          ],
          "z": [
-          34.32817459106445
+          7.712536334991455
          ]
         },
         {
@@ -2082,14 +2084,49 @@
           49.401031494140625
          ],
          "y": [
-          28.42880630493164
+          2.3271169662475586
          ],
          "z": [
-          7.20562744140625
+          12.159480094909668
          ]
         },
         {
          "facecolor": [
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+           0.09084200114011765,
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+          ],
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+           0.09084200114011765,
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+           0.7011010050773621,
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+           0.485150009393692
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+           0.7011010050773621,
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+           0.485150009393692
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+           0.5647109746932983,
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+           0.5647109746932983,
+           0.7991030216217041
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            0.21222999691963196,
            0.5647109746932983,
@@ -2101,9 +2138,39 @@
            0.05951099842786789
           ],
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-           0.6038269996643066,
-           0.09084200114011765,
-           0.04970699921250343
+           0.5088809728622437,
+           0.16826899349689484,
+           0.05951099842786789
+          ],
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+           0.21222999691963196,
+           0.5647109746932983,
+           0.7991030216217041
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+           0.21222999691963196,
+           0.5647109746932983,
+           0.7991030216217041
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+          [
+           0.7011010050773621,
+           0.644478976726532,
+           0.485150009393692
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+          [
+           0.7011010050773621,
+           0.644478976726532,
+           0.485150009393692
+          ],
+          [
+           0.7011010050773621,
+           0.644478976726532,
+           0.485150009393692
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+          [
+           0.7011010050773621,
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+           0.485150009393692
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@@ -2115,6 +2182,26 @@
            0.5647109746932983,
            0.7991030216217041
           ],
+          [
+           0.7011010050773621,
+           0.644478976726532,
+           0.485150009393692
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+           0.7011010050773621,
+           0.644478976726532,
+           0.485150009393692
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+           0.16826899349689484,
+           0.05951099842786789
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+           0.5088809728622437,
+           0.16826899349689484,
+           0.05951099842786789
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            0.5088809728622437,
            0.16826899349689484,
@@ -2125,6 +2212,36 @@
            0.16826899349689484,
            0.05951099842786789
           ],
+          [
+           0.21222999691963196,
+           0.5647109746932983,
+           0.7991030216217041
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+          [
+           0.21222999691963196,
+           0.5647109746932983,
+           0.7991030216217041
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+          [
+           0.21222999691963196,
+           0.5647109746932983,
+           0.7991030216217041
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+           0.21222999691963196,
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+           0.7991030216217041
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+          [
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+          [
+           0.21222999691963196,
+           0.5647109746932983,
+           0.7991030216217041
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            0.7011010050773621,
            0.644478976726532,
@@ -2136,9 +2253,14 @@
            0.485150009393692
           ],
           [
-           0.21222999691963196,
-           0.5647109746932983,
-           0.7991030216217041
+           0.5088809728622437,
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+           0.05951099842786789
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+           0.16826899349689484,
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@@ -2146,9 +2268,19 @@
            0.04970699921250343
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-           0.21222999691963196,
-           0.5647109746932983,
-           0.7991030216217041
+           0.6038269996643066,
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+           0.04970699921250343
+          ],
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+           0.05951099842786789
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@@ -2161,61 +2293,195 @@
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('83c9f341-42c2-450e-beda-45fbc189033a');\n",
+       "var gd = document.getElementById('e1eb76a4-3800-46eb-a767-59d6cd669f76');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -3386,8 +3693,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "a3d29b32",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3419,7 +3726,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c49db62d-621a-4013-9316-585a341c8a25",
+   "id": "11",
    "metadata": {},
    "source": [
     "## Machine Learning Model\n",
@@ -3434,8 +3741,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "c4c695c1-5a94-4feb-a767-8cea39d2c5c4",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3553,7 +3860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a60a2b7-64e8-49c1-ab70-cf327fa032f3",
+   "id": "13",
    "metadata": {},
    "source": [
     "### Outer Model\n",
@@ -3564,8 +3871,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "74d454ba",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3629,7 +3936,7 @@
     "        key: PRNGKeyArray,\n",
     "    ) -> (\n",
     "        Int[Array, \" {order}\"]\n",
-    "        | tuple[Int[Array, \" {order}\"], Float[Array, \"{order}+1 num_objects\"]]\n",
+    "        | tuple[Int[Array, \" {order}\"], Float[Array, \"{order} num_objects\"]]\n",
     "    ):\n",
     "        \"\"\"\n",
     "        Call this model to generate a path candidate of the given order.\n",
@@ -3691,12 +3998,14 @@
     "\n",
     "        # [num_triangles num_features]\n",
     "        tri_features = (\n",
+    "            # [num_triangles*3 num_features]\n",
     "            xyz_features[num_tx + num_rx :, ...]\n",
+    "            # [num_triangles 3 num_features]\n",
     "            .reshape(num_triangles, 3, *xyz_features.shape[1:])\n",
-    "            .sum(axis=1)\n",
+    "            .mean(axis=1)\n",
     "        )\n",
     "\n",
-    "        # [num_objects 1 (max_degree+1)**2 num_features]\n",
+    "        # [num_objects num_features]\n",
     "        # note: this is a no-op is 'scene.mesh.assume_quads' is False\n",
     "        object_features = tri_features.reshape(\n",
     "            num_objects, -1, *tri_features.shape[1:]\n",
@@ -3723,29 +4032,21 @@
     "            parent_edge_flow_prediction, state = carry\n",
     "            key, current_order = key_and_current_order\n",
     "\n",
-    "            # Compute probability to flow to each child state\n",
-    "            p = parent_edge_flow_prediction / jnp.sum(\n",
-    "                parent_edge_flow_prediction\n",
-    "            )\n",
-    "\n",
-    "            # Randomly choose a child state (i.e., triangle), based on 'p'\n",
+    "            # Randomly choose a child state (i.e., an object), based on forward flow\n",
+    "            p = parent_edge_flow_prediction / parent_edge_flow_prediction.sum()\n",
     "            object_index = jax.random.categorical(key=key, logits=jnp.log(p))\n",
     "\n",
     "            # Update 'state' accordingly\n",
     "            state = state.at[object_index, current_order].set(True)\n",
     "\n",
-    "            # Compute the new flow\n",
-    "            edge_flow_prediction = self.flow(\n",
-    "                scene_features, object_features, state\n",
+    "            # Compute the new flow, and stop any flow to 'object_index'\n",
+    "            # to avoid sampling the sample object twice in a row\n",
+    "            edge_flow_prediction = (\n",
+    "                self.flow(scene_features, object_features, state)\n",
+    "                .at[object_index]\n",
+    "                .set(0.0)\n",
     "            )\n",
     "\n",
-    "            # Set flow[object_index] to zero to prevent consecutive duplicate indices\n",
-    "            # A flow of zero means that there is a zero probability to pick\n",
-    "            # objects[object_index] for the next state.\n",
-    "            edge_flow_prediction = edge_flow_prediction.at[object_index].set(\n",
-    "                0.0\n",
-    "            )  # out of bounds indices are ignored\n",
-    "\n",
     "            if self.inference:\n",
     "                return (edge_flow_prediction, state), object_index\n",
     "            return (edge_flow_prediction, state), (\n",
@@ -3769,12 +4070,10 @@
     "\n",
     "        if not self.inference:\n",
     "            path_candidate, flows = path_candidate\n",
-    "            flows = jnp.vstack((parent_edge_flow_prediction, flows))\n",
+    "            flows = jnp.vstack((parent_edge_flow_prediction, flows[1:, :]))\n",
     "\n",
     "        if scene.mesh.assume_quads:\n",
-    "            path_candidate = (\n",
-    "                path_candidate * 2\n",
-    "            )  # We map quad. indices to triangles indices\n",
+    "            path_candidate *= 2\n",
     "\n",
     "        if not self.inference:\n",
     "            return path_candidate, flows\n",
@@ -3827,7 +4126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a53a4a7b-57b1-48dd-b1c0-b82fecb59514",
+   "id": "15",
    "metadata": {},
    "source": [
     "### Loss Function\n",
@@ -3841,8 +4140,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "5ee04cb5",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3883,32 +4182,31 @@
     "    ), \"Number of path candidates must be greater than zero.\"\n",
     "\n",
     "    @jaxtyped(typechecker=typechecker)\n",
-    "    def compute_flow_mismatch(\n",
-    "        key: PRNGKeyArray, order: int\n",
-    "    ) -> Float[Array, \" \"]:\n",
+    "    def compute_loss(key: PRNGKeyArray, order: int) -> Float[Array, \" \"]:\n",
     "        path_candidate, flows = model(scene, order=order, key=key)\n",
+    "        r = reward(path_candidate, scene)\n",
     "\n",
+    "        if scene.mesh.assume_quads:\n",
+    "            object_indices = path_candidate // 2\n",
+    "        else:\n",
+    "            object_indices = path_candidate\n",
+    "\n",
+    "        # [order]\n",
     "        parent_flows = jnp.take_along_axis(\n",
-    "            flows[:-1, :], path_candidate.reshape(-1, 1), axis=1\n",
+    "            flows, object_indices.reshape(-1, 1), axis=1\n",
     "        )\n",
+    "        # [order-1]\n",
     "        sum_edge_flows = flows[1:, :].sum(axis=-1)\n",
+    "        sum_edge_flows = jnp.append(sum_edge_flows, r)\n",
     "\n",
-    "        if order > 0:\n",
-    "            # For the last state, we want the parent flow to match the reward\n",
-    "            sum_edge_flows.at[-1].set(reward(path_candidate, scene))\n",
-    "\n",
-    "        # We want each parent flow to match the sum of its edge flows\n",
-    "        flow_mismatch = (parent_flows - sum_edge_flows) ** 2\n",
-    "\n",
-    "        return flow_mismatch.sum()\n",
+    "        return ((parent_flows - sum_edge_flows) ** 2.0).sum()\n",
     "\n",
     "    @jaxtyped(typechecker=typechecker)\n",
     "    def scan_path_candidates(\n",
     "        carry_loss: Float[Array, \" \"],\n",
     "        key: PRNGKeyArray,\n",
     "    ) -> tuple[Float[Array, \" \"], None]:\n",
-    "        flow_mismatch = compute_flow_mismatch(key, order)\n",
-    "        return carry_loss + flow_mismatch, None\n",
+    "        return carry_loss + compute_loss(key, order), None\n",
     "\n",
     "    carry_loss, _ = jax.lax.scan(\n",
     "        scan_path_candidates,\n",
@@ -3921,8 +4219,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "9390c534-a099-4f6e-a605-911aee0ca8f9",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3931,12 +4229,12 @@
     "    model: Model,\n",
     "    train_samples: Iterator[TriangleScene],\n",
     "    optim: optax.GradientTransformation,\n",
-    "    steps: int = 1_000,\n",
+    "    steps: int = 10_000,\n",
     "    print_every: int = 100,\n",
     "    *,\n",
     "    order: int,\n",
     "    key: PRNGKeyArray,\n",
-    ") -> tuple[Model, Float[Array, \" {steps}\"]]:\n",
+    ") -> tuple[Model, Float[Array, \" steps\"]]:\n",
     "    \"\"\"\n",
     "    Train a model on a sequence of training samples and returns the training loss average of samples.\n",
     "\n",
@@ -3997,19 +4295,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "e3842fcc-d370-42ce-8ab3-bd401995db04",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fa622ef68e9b4f6f83fb9a0a7f509296",
+       "model_id": "7d5c1ebe166244e398a81e9ad5f0dc8c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/1000 [00:00<?, ? steps/s]"
+       "  0%|          | 0/10000 [00:00<?, ? steps/s]"
       ]
      },
      "metadata": {},
@@ -4025,19 +4323,19 @@
     "optim = optax.adam(learning_rate=3e-5)\n",
     "\n",
     "trained_model, train_losses = train(\n",
-    "    untrained_model, train_samples, optim, order=1, key=key_train_model\n",
+    "    untrained_model, train_samples, optim, order=2, key=key_train_model\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "634465e2-5273-4afe-8a9d-f791ec0f68d6",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -4052,8 +4350,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('bb80ee1d-c9ef-4722-ad80-7febf32af211');\n",
+       "var gd = document.getElementById('866c5a99-c029-4095-a6c0-d0e31d33b4cc');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -5370,7 +6096,7 @@
     }
    ],
    "source": [
-    "key_eval_trained = jax.random.key(1234)\n",
+    "key_eval_trained = jax.random.key(123)\n",
     "\n",
     "key_sample_trained, key_eval_scene = jax.random.split(key_eval_trained, 2)\n",
     "\n",
@@ -5379,7 +6105,7 @@
     "    scene.plot()\n",
     "\n",
     "    path_candidates = trained_model.sample_path_candidates(\n",
-    "        scene, order=1, num_path_candidates=2, key=key_sample_trained\n",
+    "        scene, order=2, num_path_candidates=20, key=key_sample_trained\n",
     "    )\n",
     "    paths = scene.compute_paths(path_candidates=path_candidates)\n",
     "    paths.plot()\n",
@@ -5389,7 +6115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4038d16-961a-49d0-933b-a67b34171b98",
+   "id": "21",
    "metadata": {},
    "source": [
     "### Checking $\\mathrm{E}(3)$-Invariance\n",
@@ -5399,8 +6125,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "6d96effb-eff8-474e-a83d-57e377939c9b",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [
     {
@@ -6292,26 +7018,6 @@
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           "y": 1.378541948592743,
           "z": 0.6285992815097334
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-           "y": 0,
-           "z": 0
-          },
-          "eye": {
-           "x": 0.8095123675662436,
-           "y": 1.7516723220139805,
-           "z": 0.9817503771567624
-          },
-          "projection": {
-           "type": "perspective"
-          },
-          "up": {
-           "x": 0,
-           "y": 0,
-           "z": 1
-          }
          }
         },
         "template": {
@@ -7133,9 +7839,9 @@
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       },
       "text/html": [
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        "                            \n",
-       "var gd = document.getElementById('9d44ee89-d912-42ac-9d81-5db564117411');\n",
+       "var gd = document.getElementById('41db50da-519b-4fd9-a761-137699984689');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -7175,8 +7881,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "dbc054aa-81aa-45a3-8084-80f16f61f173",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [
     {
@@ -7184,9 +7890,9 @@
      "output_type": "stream",
      "text": [
       "Base scene prediction:\n",
-      " [[17, 27], [26, 45], [29, 43], [38, 66], [25, 22], [51, 25], [49, 42], [70, 61], [42, 45], [63, 20]]\n",
+      " [[8, 70], [14, 12], [54, 64], [32, 44], [64, 70], [50, 48], [14, 12], [52, 44], [24, 6], [24, 18]]\n",
       "Rotated scene prediction:\n",
-      " [[17, 27], [26, 45], [29, 43], [38, 66], [25, 22], [51, 25], [49, 42], [70, 61], [42, 45], [63, 20]]\n"
+      " [[8, 70], [14, 12], [54, 64], [32, 44], [64, 70], [50, 48], [14, 12], [52, 44], [24, 6], [24, 18]]\n"
      ]
     }
    ],
@@ -7225,7 +7931,7 @@
    "version": "3.11.8"
   },
   "mystnb": {
-    "execution_mode": "off"
+   "execution_mode": "off"
   }
  },
  "nbformat": 4,