Explaining padding_side left

Author: sergiopaniego

notebooks/en/fine_tuning_vlm_grpo_trl.ipynb

@@ -119,7 +119,7 @@
     },
     {
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-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
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           "base_uri": "https://localhost:8080/"
@@ -170,7 +170,9 @@
         "\n",
         "The system prompt is extracted from DeepSeek R1. Refer to [this previous recipe](https://huggingface.co/learn/cookbook/fine_tuning_llm_grpo_trl) for more details.\n",
         "\n",
-        "We convert the dataset samples into conversation samples, including the system prompt and one image and problem description per sample, since this is how the GRPO trainer expects them."
+        "We convert the dataset samples into conversation samples, including the system prompt and one image and problem description per sample, since this is how the GRPO trainer expects them.\n",
+        "\n",
+        "We also set `padding_side=\"left\"` to ensure that generated completions during trainig are concatenated directly after the prompt, which is essential for GRPO to correctly compare token-level probabilities between preferred and rejected responses."
       ],
       "metadata": {
         "id": "6isapXWue91d"
@@ -217,7 +219,7 @@
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         "outputId": "9255ad62-3fa1-4578-85d7-15942ec3bd35"
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-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -249,7 +251,7 @@
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         "outputId": "5fa28404-101d-4232-bbde-fdb51a60c0de"
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-      "execution_count": 5,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -292,7 +294,7 @@
           "base_uri": "https://localhost:8080/"
         }
       },
-      "execution_count": 6,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -331,7 +333,7 @@
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         "outputId": "e805bb0c-3c6d-4bf4-d1b3-a8ae7190bf2b"
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-      "execution_count": 7,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -422,7 +424,7 @@
           ]
         }
       },
-      "execution_count": 8,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -453,7 +455,7 @@
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     {
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-      "execution_count": 9,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -517,7 +519,7 @@
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-      "execution_count": 10,
+      "execution_count": null,
       "outputs": []
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@@ -584,7 +586,7 @@
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-      "execution_count": 11,
+      "execution_count": null,
       "outputs": []
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@@ -602,7 +604,7 @@
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-      "execution_count": 12,
+      "execution_count": null,
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@@ -659,7 +661,7 @@
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+      "execution_count": null,
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         "outputId": "c42b4d97-9692-48d3-f03a-b1eaf5687559",
@@ -710,7 +712,7 @@
           "height": 1000
         }
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-      "execution_count": 15,
+      "execution_count": null,
       "outputs": [
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@@ -894,7 +896,7 @@
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-      "execution_count": 16,
+      "execution_count": null,
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@@ -940,7 +942,7 @@
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-      "execution_count": 17,
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@@ -1029,7 +1031,7 @@
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-      "execution_count": 18,
+      "execution_count": null,
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@@ -1171,7 +1173,7 @@
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-      "execution_count": 19,
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@@ -1196,7 +1198,7 @@
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@@ -1245,7 +1247,7 @@
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+      "execution_count": null,
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@@ -1284,7 +1286,7 @@
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