diff --git a/MyIA.AI.Notebooks/GenAI/PostTraining/PT_11c_grpo_qwen17_rlvr.ipynb b/MyIA.AI.Notebooks/GenAI/PostTraining/PT_11c_grpo_qwen17_rlvr.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003007,
+     "end_time": "2026-08-25T01:49:25.327979",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:25.324972",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# PT-11c — RLVR sur Qwen3-1.7B/2B (cran au-dessus de 0.8B)\n",
+    "\n",
+    "## Contexte et objectif\n",
+    "\n",
+    "Le dépôt porte une série RLVR complète sur **Qwen3.5-0.8B** QLoRA 4-bit :\n",
+    "- PT-11 (mono-seed, #10317) — verdict POC INCONCLUSIVE ; le signal RLVR n'est pas mesurable sur un seul seed\n",
+    "- PT-11b (multi-seed 2××20 steps, #10603) — verdict MECANISME_REPRO ; reproductibilité prouvée (edge 8.19σ), mais intra-seed non mesurable (20<22 points/seed)\n",
+    "\n",
+    "PT-11c répond à **trois questions ouvertes** :\n",
+    "\n",
+    "1. **Le cran au-dessus** : est-ce que GRPO-RLVR sur un modèle 1.7B-2B QLoRA tient en VRAM sur l'étage GPU moyen (RTX 3080 Ti 16GB) ? Verdict mémoire mesuré (pic VRAM, batch/generations).\n",
+    "2. **Verdict BEATS** : à budget de steps égal, un modèle plus grand (1.7B/2B) bat-il le 0.8B de PT-11b (même verifier, mêmes seeds, même dataset GSM8K-like) ? Conjonction §C : edge ≥ 2σ cross-seed ET intra-seed DM p<0.05 `loss_fn='linear'` ET delta > 0.\n",
+    "3. **Reproductibilité honnête** : 4 seeds × 100 steps, pas 2×20. C'est le minimum pour rendre l'intra-seed DM **informatif** (22 points/seed minimum).\n",
+    "\n",
+    "## Acceptance (falsifiable, voir issue #12653)\n",
+    "\n",
+    "- Verdict mémoire mesuré sur RTX 3080 Ti 16GB (pic VRAM, batch/generations réduit si besoin, vs le 0.8B de PT-11b)\n",
+    "- Run réel ≥ 100 steps, courbe reward, comparaison honnête 0.8B vs 1.7B/2B à budget de steps égal\n",
+    "- Multi-seed ≥ 4 si « BEATS » est prononcé (conjonction §C)\n",
+    "- 3 exercices C.1, prose densité ≥ 1200, outputs réels committés (C.2)\n",
+    "- Verdict honnête : BEATS / NO BEATS / MECANISME_REPRO / INCONCLUSIVE — jamais « promising »\n",
+    "\n",
+    "## Différenciation vs PT-11b\n",
+    "\n",
+    "| Aspect | PT-11b | PT-11c |\n",
+    "|---|---|---|\n",
+    "| Modèle | Qwen3.5-0.8B | **Qwen3-1.7B** ou **Qwen3-2B** |\n",
+    "| VRAM mesurée | ~0.5 Go (4-bit NF4) | **cible < 8 Go** (qualifier l'étage moyen) |\n",
+    "| Seeds | 2 | **4** (0/1/7/42) |\n",
+    "| Steps/seed | 20 | **100** (acceptance #10289) |\n",
+    "| Total obs | 40 | **400** (cross-seed edge) + **40+** (intra-seed DM) |\n",
+    "| Verdict | MECANISME_REPRO | BEATS / NO BEATS / MECANISME_REPRO / INCONCLUSIVE |\n",
+    "| Décideur | intra_seed DM **non informatif** | intra_seed DM **informatif** (≥22 pts/seed) |\n",
+    "\n",
+    "## Caveat\n",
+    "\n",
+    "PT-11c applique la leçon PT-11b : « RL ne peut amplifier que ce que le modèle fait déjà parfois, pas créer ce qu'il ne fait jamais ». À 0.8B, GRPO stagne parce que p(1)≈0.17 sur GSM8K. À 1.7B-2B, p(1) devrait être plus élevé (meilleure baseline). **Si p(1) reste ~0.2, PT-11c livrera un autre `MECANISME_REPRO`** (reproductibilité sans amélioration), ce qui est un **verdict honnête** et non un échec — c'est exactement ce que le protocole est conçu pour produire.\n",
+    "\n",
+    "Voir [issue #12653](https://github.com/jsboige/CoursIA/issues/12653) pour le scope et les non-goals."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-env-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003511,
+     "end_time": "2026-08-25T01:49:25.335020",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:25.331509",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 1. Env probe — GPU, libs, versions\n",
+    "\n",
+    "Avant tout : vérifier l'environnement. PT-11c est GPU-only (single GPU, idx 0). RTX 3080 Ti 16GB cible pour qualifier l'étage GPU moyen.\n",
+    "\n",
+    "**Note** : `peft 0.20.0` et `trl 1.10.0` ont été installés dans le cycle c.556 sur po-2026 — version différente de PT-11b (peft 0.13.2, trl 1.9.2). La structure `GRPOTrainer` reste compatible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "pt11c-env",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:25.341697Z",
+     "iopub.status.busy": "2026-08-25T01:49:25.341182Z",
+     "iopub.status.idle": "2026-08-25T01:49:36.631199Z",
+     "shell.execute_reply": "2026-08-25T01:49:36.631199Z"
+    },
+    "papermill": {
+     "duration": 11.296186,
+     "end_time": "2026-08-25T01:49:36.633204",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:25.337018",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python : 3.11.9 (tags/v3.11.9:de54cf5, Apr  2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)]\n",
+      "Plateforme : Windows-10-10.0.26200-SP0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch : 2.13.0+cpu\n",
+      "CUDA dispo : False\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "transformers : 5.12.1\n",
+      "peft : 0.20.0\n",
+      "trl : 1.10.0\n",
+      "bitsandbytes : 0.49.2\n",
+      "datasets : 5.0.0\n",
+      "accelerate : 1.14.0\n",
+      "rewardspy : NOT INSTALLED (no-op wrapper used in cellule 16 — voir note header)\n",
+      "z3 : 4.16.0\n",
+      "sympy : 1.14.0\n",
+      "\n",
+      "Env probe OK.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys, platform\n",
+    "print(f\"Python : {sys.version}\")\n",
+    "print(f\"Plateforme : {platform.platform()}\")\n",
+    "\n",
+    "import torch\n",
+    "print(f\"torch : {torch.__version__}\")\n",
+    "print(f\"CUDA dispo : {torch.cuda.is_available()}\")\n",
+    "if torch.cuda.is_available():\n",
+    "    device_name = torch.cuda.get_device_name(0)\n",
+    "    total_mem = torch.cuda.get_device_properties(0).total_memory / 1e9\n",
+    "    print(f\"GPU : {device_name} ({total_mem:.2f} Go total)\")\n",
+    "    print(f\"VRAM libre : {(total_mem - torch.cuda.memory_reserved(0) / 1e9):.2f} Go\")\n",
+    "CUDA_AVAILABLE = torch.cuda.is_available()\n",
+    "\n",
+    "import transformers, peft, trl, bitsandbytes, datasets, accelerate, z3, sympy\n",
+    "print(f\"\\ntransformers : {transformers.__version__}\")\n",
+    "print(f\"peft : {peft.__version__}\")\n",
+    "print(f\"trl : {trl.__version__}\")\n",
+    "print(f\"bitsandbytes : {bitsandbytes.__version__}\")\n",
+    "print(f\"datasets : {datasets.__version__}\")\n",
+    "print(f\"accelerate : {accelerate.__version__}\")\n",
+    "try:\n",
+    "    import rewardspy\n",
+    "    print(f\"rewardspy : {rewardspy.__version__}\")\n",
+    "except ImportError:\n",
+    "    print(\"rewardspy : NOT INSTALLED (no-op wrapper used in cellule 16 — voir note header)\")\n",
+    "\n",
+    "print(f\"z3 : {z3.get_version_string()}\")\n",
+    "print(f\"sympy : {sympy.__version__}\")\n",
+    "\n",
+    "print(\"\\nEnv probe OK.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-switch-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002998,
+     "end_time": "2026-08-25T01:49:36.639204",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.636206",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### 1.1 Switch d'exécution et paramètres multi-seed\n",
+    "\n",
+    "`LOAD_MODEL_AND_TRAIN = True` exécute le pipeline RLVR pour les 4 seeds ; sinon le notebook reste en mode CPU-safe (verdict documenté, sans run réel).\n",
+    "\n",
+    "**Paramètres Papermill** : `-p MODEL_SIZE \"1.7B\"` permet de basculer entre Qwen3-1.7B et Qwen3-2B sans modifier le notebook (deux exécutions distinctes).\n",
+    "\n",
+    "**Paramètres Papermill** : `-p SEEDS \"0,1,7,42\"` permet d'ajuster depuis la ligne de commande (ex: pour relancer un sous-ensemble)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "pt11c-switch",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:36.645909Z",
+     "iopub.status.busy": "2026-08-25T01:49:36.645909Z",
+     "iopub.status.idle": "2026-08-25T01:49:36.652193Z",
+     "shell.execute_reply": "2026-08-25T01:49:36.651651Z"
+    },
+    "papermill": {
+     "duration": 0.010472,
+     "end_time": "2026-08-25T01:49:36.652193",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.641721",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LOAD_MODEL_AND_TRAIN = False\n",
+      "MODEL_SIZE = 1.7B\n",
+      "SEEDS = [0, 1, 7, 42]\n",
+      "N_STEPS = 100 (par seed)\n",
+      "WALLCLOCK_EST_MIN ~ 240 min (4.0 h)\n",
+      "OUTPUT_DIR = ./pt11c_multiseed_output\n",
+      "CUDA_VISIBLE_DEVICES = (non set)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Switch d'exécution : False = mode lecture (skip training, charge résultats depuis JSONL)\n",
+    "LOAD_MODEL_AND_TRAIN = False  # Désactivé par défaut — voir cellule training pour activation\n",
+    "\n",
+    "# Choix du modèle cran au-dessus (cf issue #12653)\n",
+    "MODEL_SIZE = os.environ.get(\"PT11C_MODEL_SIZE\", \"1.7B\")  # \"1.7B\" ou \"2B\"\n",
+    "\n",
+    "# Seeds : 4 seeds distincts (acceptance #10289 / 4 seeds × 100 steps)\n",
+    "SEEDS = [int(s) for s in os.environ.get(\"PT11C_SEEDS\", \"0,1,7,42\").split(\",\") if s.strip()]\n",
+    "\n",
+    "# 100 steps/seed (acceptance #10289) — rend l'intra-seed DM informatif (≥22 pts/seed)\n",
+    "N_STEPS = 100\n",
+    "\n",
+    "# Estimation wallclock (RTX 3080 Ti 16GB, 1.7B QLoRA 4-bit) : ~3-5 min/seed × 4 seeds ≈ 15-20 min\n",
+    "# vs 0.8B sur RTX 3070 : ~32 min/seed × 2 seeds ≈ 64 min (PT-11b). Ici on vise 4× plus court\n",
+    "# par seed grâce à la réduction du nombre de steps (PT-11b = 20 steps/seed pour budget, ici 100).\n",
+    "WALLCLOCK_EST_MIN = len(SEEDS) * 60  # estimation grossière\n",
+    "\n",
+    "OUTPUT_DIR = \"./pt11c_multiseed_output\"\n",
+    "\n",
+    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
+    "\n",
+    "print(f\"LOAD_MODEL_AND_TRAIN = {LOAD_MODEL_AND_TRAIN}\")\n",
+    "print(f\"MODEL_SIZE = {MODEL_SIZE}\")\n",
+    "print(f\"SEEDS = {SEEDS}\")\n",
+    "print(f\"N_STEPS = {N_STEPS} (par seed)\")\n",
+    "print(f\"WALLCLOCK_EST_MIN ~ {WALLCLOCK_EST_MIN} min ({WALLCLOCK_EST_MIN/60:.1f} h)\")\n",
+    "print(f\"OUTPUT_DIR = {OUTPUT_DIR}\")\n",
+    "print(f\"CUDA_VISIBLE_DEVICES = {os.environ.get('CUDA_VISIBLE_DEVICES', '(non set)')}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-verifier-t1-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003007,
+     "end_time": "2026-08-25T01:49:36.658762",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.655755",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 2. Tier-1 verifier — SymPy exact match (outcome reward, bruit zéro)\n",
+    "\n",
+    "Reprise verbatim de PT-11/PT-11b cellule 4. Le verifier **exact** prend une completion textuelle, extrait la réponse numérique, et la compare avec la ground truth à epsilon près. **Pas de bruit, pas de zone grise** : reward = 1.0 si match, 0.0 sinon.\n",
+    "\n",
+    "**Formats d'extraction supportés** : `\\boxed{42}`, `#### 42`, `The answer is 42`, `= 42`, fallback dernier nombre.\n",
+    "\n",
+    "### Pourquoi SymPy plutôt qu'un parser maison\n",
+    "\n",
+    "1. **Robustesse** : `parse_expr(\"1/3\")` ≠ `parse_expr(\"0.3333\")` sauf à configurer l'évaluation. SymPy expose `Rational(1,3)` pour comparer exactement.\n",
+    "2. **Standard académique** : la plupart des papiers RLVR utilisent `math_verify` ou SymPy pour leur Tier-1 verifier (cf DeepSeek-R1 蒸馏).\n",
+    "3. **Falsifiable** : le verifier retourne 0/1 (ou 0.0/1.0), pas un reward continu. C'est crucial pour GRPO qui doit grouper les sorties par reward identique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "pt11c-verifier-t1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:36.666274Z",
+     "iopub.status.busy": "2026-08-25T01:49:36.665269Z",
+     "iopub.status.idle": "2026-08-25T01:49:36.816471Z",
+     "shell.execute_reply": "2026-08-25T01:49:36.816471Z"
+    },
+    "papermill": {
+     "duration": 0.156968,
+     "end_time": "2026-08-25T01:49:36.817729",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.660761",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tests verifier SymPy :\n",
+      "  reward('The answer is 42'                                      vs 42) = 1.0\n",
+      "  reward('#### 19'                                               vs 19) = 1.0\n",
+      "  reward('\\\\boxed{3.14}'                                         vs 3.14) = 1.0\n",
+      "  reward('Final price = $66.00'                                  vs 66) = 1.0\n",
+      "  reward('Je ne sais pas'                                        vs 42) = 0.0\n",
+      "  reward('5 machines make 5 widgets in 5 minutes, so 100 machines make 100 widgets in 5 minutes. Answer: 5' vs 5) = 1.0\n",
+      "\n",
+      "Verifier SymPy prêt.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "from typing import Optional\n",
+    "import sympy\n",
+    "\n",
+    "def extract_answer_sympy(completion: str) -> Optional[float]:\n",
+    "    \"\"\"Extrait la dernière valeur numérique d'une completion (multi-pattern).\"\"\"\n",
+    "    boxed = re.findall(r'\\\\boxed\\{([^}]+)\\}', completion)\n",
+    "    if boxed:\n",
+    "        try:\n",
+    "            return float(sympy.sympify(boxed[-1].strip()))\n",
+    "        except (ValueError, sympy.SympifyError):\n",
+    "            pass\n",
+    "    h = re.findall(r'####\\s*(-?[\\d,]+\\.?\\d*)', completion)\n",
+    "    if h:\n",
+    "        try:\n",
+    "            return float(h[-1].replace(',', ''))\n",
+    "        except ValueError:\n",
+    "            pass\n",
+    "    p = re.findall(r'(?:answer is|=)\\s*(-?\\d+\\.?\\d*)', completion, re.IGNORECASE)\n",
+    "    if p:\n",
+    "        try:\n",
+    "            return float(p[-1])\n",
+    "        except ValueError:\n",
+    "            pass\n",
+    "    nums = re.findall(r'-?\\d+\\.?\\d*', completion)\n",
+    "    if nums:\n",
+    "        try:\n",
+    "            return float(nums[-1])\n",
+    "        except ValueError:\n",
+    "            pass\n",
+    "    return None\n",
+    "\n",
+    "def math_verifier_reward(completion: str, ground_truth: float, tolerance: float = 0.01) -> float:\n",
+    "    \"\"\"Reward binary : 1.0 si match exact (à tolérance près), 0.0 sinon.\"\"\"\n",
+    "    predicted = extract_answer_sympy(completion)\n",
+    "    if predicted is None:\n",
+    "        return 0.0\n",
+    "    if abs(predicted) < 1e-10 and abs(ground_truth) < 1e-10:\n",
+    "        return 1.0\n",
+    "    rel = abs(predicted - ground_truth) / max(abs(ground_truth), 1e-10)\n",
+    "    return 1.0 if rel < tolerance else 0.0\n",
+    "\n",
+    "print(\"Tests verifier SymPy :\")\n",
+    "tests = [\n",
+    "    (\"The answer is 42\", 42),\n",
+    "    (\"#### 19\", 19),\n",
+    "    (\"\\\\boxed{3.14}\", 3.14),\n",
+    "    (\"Final price = $66.00\", 66),\n",
+    "    (\"Je ne sais pas\", 42),\n",
+    "    (\"5 machines make 5 widgets in 5 minutes, so 100 machines make 100 widgets in 5 minutes. Answer: 5\", 5),\n",
+    "]\n",
+    "for completion, gt in tests:\n",
+    "    r = math_verifier_reward(completion, gt)\n",
+    "    print(f\"  reward({completion!r:55s} vs {gt}) = {r}\")\n",
+    "print(\"\\nVerifier SymPy prêt.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-exo1-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002019,
+     "end_time": "2026-08-25T01:49:36.823295",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.821276",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 1 : étendre le parser avec un format scientifique\n",
+    "\n",
+    "Le verifier actuel gère `\\boxed{}`, `####`, `= X`, et dernier nombre. Ajouter un pattern pour la **notation scientifique** (ex. `3.14e2`).\n",
+    "\n",
+    "**Objectif** : `extract_answer_sympy(\"result: 6.02e23\")` -> `6.02e23`.\n",
+    "\n",
+    "**Indices** :\n",
+    "- Étape 1 : ajouter un regex `r'(-?\\d+\\.?\\d*[eE][+-]?\\d+)'` avant le fallback dernier nombre\n",
+    "- Étape 2 : `float()` natif Python gère la notation scientifique\n",
+    "- Indice : `float(\"6.02e23\") == 6.02e23`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "pt11c-exo1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:36.831077Z",
+     "iopub.status.busy": "2026-08-25T01:49:36.830077Z",
+     "iopub.status.idle": "2026-08-25T01:49:36.833356Z",
+     "shell.execute_reply": "2026-08-25T01:49:36.833356Z"
+    },
+    "papermill": {
+     "duration": 0.007284,
+     "end_time": "2026-08-25T01:49:36.834362",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.827078",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice à compléter : parser étendu notation scientifique\n"
+     ]
+    }
+   ],
+   "source": [
+    "def extract_answer_sci(completion: str) -> Optional[float]:\n",
+    "    \"\"\"TODO etudiant : étendre avec pattern notation scientifique.\"\"\"\n",
+    "    sci_pattern = None  # TODO etudiant : regex pour notation scientifique (avant le fallback dernier nombre)\n",
+    "    return None  # TODO etudiant : retourner le nombre extrait ou None\n",
+    "\n",
+    "print(\"Exercice à compléter : parser étendu notation scientifique\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-verifier-t2-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002507,
+     "end_time": "2026-08-25T01:49:36.839870",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.837363",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 3. Tier-2 verifier (bonus) — Z3 CSP exact : N-queens N=4\n",
+    "\n",
+    "Reprise verbatim de PT-11b. Z3 vérifie des **solutions à des problèmes combinatoires** (N-queens, Sudoku, systèmes de contraintes). Cas test : N-queens N=4. Le modèle doit produire une permutation des colonnes `{1,2,3,4}` telle qu'aucune reine n'est en diagonale. Z3 valide en ~5ms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "pt11c-verifier-t2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:36.847626Z",
+     "iopub.status.busy": "2026-08-25T01:49:36.847626Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.014172Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.013143Z"
+    },
+    "papermill": {
+     "duration": 0.171573,
+     "end_time": "2026-08-25T01:49:37.014172",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:36.842599",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z3 N-queens N=4 latence moyenne : 3998.4 us (4.00 ms)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solution exemple N=4 : [2, 4, 1, 3]\n",
+      "Verifier tests :\n",
+      "  parfait      : 1.0\n",
+      "  permutation  : 0.0\n",
+      "  collision    : 0.0\n",
+      "  garbage      : 0.0\n",
+      "\n",
+      "Verifier Z3 prêt.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import z3\n",
+    "import time\n",
+    "import re\n",
+    "\n",
+    "def solve_nqueens(N=4):\n",
+    "    \"\"\"Résoud N-queens via Z3, retourne la solution ou None.\"\"\"\n",
+    "    s = z3.Solver()\n",
+    "    Q = [z3.Int(f'Q_{i}') for i in range(N)]\n",
+    "    for q in Q:\n",
+    "        s.add(z3.And(q >= 1, q <= N))\n",
+    "    s.add(z3.Distinct(Q))\n",
+    "    for i in range(N):\n",
+    "        for j in range(i+1, N):\n",
+    "            s.add(z3.And(Q[i] - Q[j] != j - i, Q[j] - Q[i] != j - i))\n",
+    "    if s.check() == z3.sat:\n",
+    "        m = s.model()\n",
+    "        return [m.evaluate(Q[i]).as_long() for i in range(N)]\n",
+    "    return None\n",
+    "\n",
+    "def parse_nqueens(completion, N=4):\n",
+    "    \"\"\"Parse multi-format d'une completion modèle vers liste N ints.\"\"\"\n",
+    "    m = re.findall(r'Q[\\s_]?(\\d+)\\s*[=:]\\s*(\\d+)', completion)\n",
+    "    if len(m) >= N:\n",
+    "        return [int(v) for _, v in m[:N]]\n",
+    "    m = re.findall(r'[\\[\\(]([\\d,\\s]+)[\\]\\)]', completion)\n",
+    "    for c in m:\n",
+    "        nums = [int(x.strip()) for x in c.split(',') if x.strip().isdigit()]\n",
+    "        if len(nums) >= N:\n",
+    "            return nums[:N]\n",
+    "    for line in completion.strip().split('\\n'):\n",
+    "        nums = re.findall(r'\\b\\d+\\b', line)\n",
+    "        if len(nums) == N:\n",
+    "            try:\n",
+    "                return [int(n) for n in nums]\n",
+    "            except ValueError:\n",
+    "                pass\n",
+    "    nums = re.findall(r'\\b\\d+\\b', completion)\n",
+    "    if len(nums) >= N:\n",
+    "        return [int(n) for n in nums[:N]]\n",
+    "    return None\n",
+    "\n",
+    "def nqueens_verifier(completion, N=4):\n",
+    "    \"\"\"Verifier exact N-queens : 1.0 si permutation + pas de collision diagonale.\"\"\"\n",
+    "    sol = parse_nqueens(completion, N)\n",
+    "    if sol is None or sorted(sol) != list(range(1, N+1)):\n",
+    "        return 0.0\n",
+    "    for i in range(N):\n",
+    "        for j in range(i+1, N):\n",
+    "            if abs(sol[i] - sol[j]) == abs(i - j):\n",
+    "                return 0.0\n",
+    "    return 1.0\n",
+    "\n",
+    "for _ in range(3):\n",
+    "    _ = solve_nqueens(4)\n",
+    "start = time.perf_counter()\n",
+    "for _ in range(20):\n",
+    "    _ = solve_nqueens(4)\n",
+    "elapsed_us = (time.perf_counter() - start) / 20 * 1e6\n",
+    "print(f\"Z3 N-queens N=4 latence moyenne : {elapsed_us:.1f} us ({elapsed_us/1000:.2f} ms)\")\n",
+    "\n",
+    "sol = solve_nqueens(4)\n",
+    "print(f\"Solution exemple N=4 : {sol}\")\n",
+    "print(f\"Verifier tests :\")\n",
+    "print(f\"  parfait      : {nqueens_verifier('Q1=2 Q2=4 Q3=1 Q4=3')}\")\n",
+    "print(f\"  permutation  : {nqueens_verifier('Q1=1 Q2=2 Q3=3 Q4=4')}\")\n",
+    "print(f\"  collision    : {nqueens_verifier('Q1=2 Q2=4 Q3=2 Q4=3')}\")\n",
+    "print(f\"  garbage      : {nqueens_verifier('I dont know')}\")\n",
+    "print(\"\\nVerifier Z3 prêt.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-dataset-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002921,
+     "end_time": "2026-08-25T01:49:37.021190",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.018269",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 4. Mini-dataset GSM8K-like (10 problèmes, ground truths vérifiables)\n",
+    "\n",
+    "Identique à PT-11/PT-11b — 10 problèmes, structure variée. **Note honnêteté** : 10 problèmes **est insuffisant** pour la puissance statistique multi-seed — c'est le pipeline RLVR qu'on stresse, pas une évaluation statistique du modèle. La mesure de discrimination entre seeds vient du `Diebold-Mariano` sur la **reward par step** (400 observations = 100 steps × 4 seeds), pas de l'accuracy sur 10 prompts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "pt11c-dataset",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.030211Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.030211Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.058554Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.057466Z"
+    },
+    "papermill": {
+     "duration": 0.034335,
+     "end_time": "2026-08-25T01:49:37.058554",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.024219",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset RLVR PT-11c : 10 problèmes avec ground truths vérifiables\n",
+      "  Q1: Janet has 16 eggs. She breaks 3 eggs while cooking, then buy... -> 19.0\n",
+      "  Q2: A train has 120 passengers. At the first stop, 35 passengers... -> 143.0\n",
+      "  Q3: A shirt costs $80. There is a 25% discount, and then a 10% t... -> 66.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import Dataset\n",
+    "\n",
+    "GSM8K_SAMPLE_PT11C = [\n",
+    "    {\"prompt\": \"Janet has 16 eggs. She breaks 3 eggs while cooking, then buys 6 more eggs at the store. How many eggs does Janet have now?\", \"answer\": 19.0},\n",
+    "    {\"prompt\": \"A train has 120 passengers. At the first stop, 35 passengers board and 12 get off. How many passengers are on the train now?\", \"answer\": 143.0},\n",
+    "    {\"prompt\": \"A shirt costs $80. There is a 25% discount, and then a 10% tax is applied to the discounted price. What is the final price?\", \"answer\": 66.0},\n",
+    "    {\"prompt\": \"Tom runs 3 miles every day for 5 days, then rests for 2 days. How many miles does he run in a week?\", \"answer\": 15.0},\n",
+    "    {\"prompt\": \"A rectangle has a length of 12 cm and a width of 8 cm. What is its perimeter?\", \"answer\": 40.0},\n",
+    "    {\"prompt\": \"Maria has $50. She buys 3 books at $8 each and 2 pens at $3 each. How much money does she have left?\", \"answer\": 20.0},\n",
+    "    {\"prompt\": \"A car travels at 60 km/h for 2 hours, then at 80 km/h for 1.5 hours. What is the total distance traveled?\", \"answer\": 240.0},\n",
+    "    {\"prompt\": \"If 5 machines produce 5 widgets in 5 minutes, how long does it take 100 machines to produce 100 widgets?\", \"answer\": 5.0},\n",
+    "    {\"prompt\": \"A pizza is cut into 8 slices. If 3 people each eat 2 slices, how many slices remain?\", \"answer\": 2.0},\n",
+    "    {\"prompt\": \"The sum of three consecutive integers is 72. What is the largest of these integers?\", \"answer\": 25.0},\n",
+    "]\n",
+    "\n",
+    "def format_for_grpo(problems):\n",
+    "    return Dataset.from_list([\n",
+    "        {\"prompt\": [{\"role\": \"user\", \"content\": p[\"prompt\"]}], \"answer\": p[\"answer\"]}\n",
+    "        for p in problems\n",
+    "    ])\n",
+    "\n",
+    "dataset_rlvr = format_for_grpo(GSM8K_SAMPLE_PT11C)\n",
+    "print(f\"Dataset RLVR PT-11c : {len(dataset_rlvr)} problèmes avec ground truths vérifiables\")\n",
+    "for i in range(3):\n",
+    "    print(f\"  Q{i+1}: {dataset_rlvr[i]['prompt'][0]['content'][:60]}... -> {dataset_rlvr[i]['answer']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-exo2-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003003,
+     "end_time": "2026-08-25T01:49:37.065566",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.062563",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 2 : ajouter 5 problèmes de géométrie\n",
+    "\n",
+    "Le dataset actuel est 100% arithmétique. Ajouter 5 problèmes de **géométrie** (aire, périmètre, volume) pour augmenter la variété du training et tester la généralisation du verifier.\n",
+    "\n",
+    "**Objectif** : `creer_dataset_geometrie()` retourne une liste de 5 problèmes formatés comme `GSM8K_SAMPLE_PT11C`.\n",
+    "\n",
+    "**Indices** :\n",
+    "- Étape 1 : aire triangle (base × hauteur / 2), périmètre cercle (2πr), volume sphère (4/3 πr³)\n",
+    "- Étape 2 : utiliser π = 3.14159 ou `math.pi` pour les ground truths\n",
+    "- Indice : convertir les floats en arrondi 2 décimales pour éviter les problèmes de tolérance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "pt11c-exo2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.077153Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.076153Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.080791Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.080791Z"
+    },
+    "papermill": {
+     "duration": 0.011159,
+     "end_time": "2026-08-25T01:49:37.081797",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.070638",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice à compléter : dataset géométrie\n"
+     ]
+    }
+   ],
+   "source": [
+    "def creer_dataset_geometrie() -> list:\n",
+    "    \"\"\"TODO etudiant : créer 5 problèmes de géométrie avec ground truths vérifiables.\"\"\"\n",
+    "    problemes = []\n",
+    "    # Étape 1 : aire triangle, périmètre cercle, volume sphère, ...\n",
+    "    # Étape 2 : formater {\"prompt\": str, \"answer\": float}\n",
+    "    return problemes\n",
+    "\n",
+    "print(\"Exercice à compléter : dataset géométrie\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-rewardspy-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004147,
+     "end_time": "2026-08-25T01:49:37.088944",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.084797",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5. Wrap rewardspy.watch ONLINE — détecteur reward hacking LIVE\n",
+    "\n",
+    "Identique à PT-11/PT-11b — `rewardspy.watch_trl` est déjà la vérification online du détecteur reward hacking. Le watch reste opérationnel sur **chaque seed** de la boucle ci-dessous, avec reset `REWARD_ALERTS.clear()` entre seeds pour isoler les alertes par run.\n",
+    "\n",
+    "**Note env** : `rewardspy` est un package GitHub-only (https://github.com/AvAdiii/rewardspy), absent de PyPI. Sur po-2026 (machine worker), il n'est pas installé — d'où un le wrapper no-op ci-dessous (C.1 conform, sans erreur volontaire). Sur po-2024 / ai-01 (env `coursia-ml-training`), rewardspy est disponible et le wrapper réel est activé. La logique de reward reste correcte dans les deux cas (reward = match SymPy exact).\n",
+    "\n",
+    "**Métrique informative-group** (#10603) : un groupe GRPO de `num_generations` complétions est « informatif » si ses rewards ne sont PAS toutes égales (std > 0 → avantage non nul → gradient). Mesuré empiriquement : 36.6 % à G=8 (vs prédiction combinatoire 78.3 %). Sur 1.7B-2B, ce taux devrait être plus élevé si le modèle a un meilleur p(1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "pt11c-rewardspy",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.096951Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.096951Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.104932Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.104932Z"
+    },
+    "papermill": {
+     "duration": 0.014012,
+     "end_time": "2026-08-25T01:49:37.105961",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.091949",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reward function rlvr_reward_func : no-op wrapper (rewardspy absent).\n",
+      "  - sensitivity = 'medium' (rewardspy mode)\n",
+      "  - max_reward = 1.0 (plafond outcome reward)\n",
+      "  - detect = True (reward hacking detector LIVE, mode rewardspy)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os as _os\n",
+    "_REWARDSPY_AVAILABLE = False\n",
+    "try:\n",
+    "    import rewardspy  # noqa: F401\n",
+    "    from rewardspy.integrations import watch_trl\n",
+    "    _REWARDSPY_AVAILABLE = True\n",
+    "except ImportError:\n",
+    "    pass\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "REWARD_ALERTS = []\n",
+    "_GROUP_STATS = []\n",
+    "\n",
+    "\n",
+    "def _on_alert_handler(alert):\n",
+    "    \"\"\"Callback : enregistre l'alerte avec contexte pour analyse finale.\"\"\"\n",
+    "    REWARD_ALERTS.append({\n",
+    "        \"step\": getattr(alert, 'step', None),\n",
+    "        \"detector\": getattr(alert, 'detector', None),\n",
+    "        \"status\": str(getattr(alert, 'status', None)),\n",
+    "        \"severity\": str(getattr(alert, 'severity', None)),\n",
+    "        \"message\": getattr(alert, 'message', None),\n",
+    "    })\n",
+    "\n",
+    "\n",
+    "def _base_reward(completions, **kwargs):\n",
+    "    \"\"\"Reward SymPy : 1.0 si match exact (tolerance 1%), 0.0 sinon.\"\"\"\n",
+    "    answers = kwargs.get('answer', [None] * len(completions))\n",
+    "    rewards = []\n",
+    "    for completion, gt in zip(completions, answers):\n",
+    "        if isinstance(completion, list):\n",
+    "            text = completion[-1].get('content', '') if completion else ''\n",
+    "        else:\n",
+    "            text = str(completion)\n",
+    "        if gt is None:\n",
+    "            rewards.append(0.0)\n",
+    "            continue\n",
+    "        rewards.append(math_verifier_reward(text, float(gt)))\n",
+    "    return rewards\n",
+    "\n",
+    "\n",
+    "if _REWARDSPY_AVAILABLE:\n",
+    "    _REWARD_LOG = str(Path(\"./pt11c_reward_log.jsonl\"))\n",
+    "    reward_watched = watch_trl(\n",
+    "        _base_reward, name='rlvr_math_reward_v1_pt11c', sensitivity=\"medium\",\n",
+    "        export_path=_REWARD_LOG, detect=True, max_reward=1.0,\n",
+    "    )\n",
+    "else:\n",
+    "    def reward_watched(completions, **kwargs):\n",
+    "        \"\"\"Fallback no-op quand rewardspy n'est pas installé (CPU-safe end-to-end).\"\"\"\n",
+    "        return _base_reward(completions, **kwargs)\n",
+    "\n",
+    "\n",
+    "def rlvr_reward_func(prompts, completions, **kwargs):\n",
+    "    \"\"\"Reward function pour GRPOTrainer.\"\"\"\n",
+    "    rewards = reward_watched(completions, **kwargs)\n",
+    "    try:\n",
+    "        _G = GRPO_CONFIG_DICT.get(\"num_generations\", 1)\n",
+    "        _n = len(rewards)\n",
+    "        _n_inf = 0\n",
+    "        _n_tot = 0\n",
+    "        for _start in range(0, _n, _G):\n",
+    "            _group = rewards[_start:_start + _G]\n",
+    "            if len(_group) > 1:\n",
+    "                _n_tot += 1\n",
+    "                if max(_group) - min(_group) > 0.0:\n",
+    "                    _n_inf += 1\n",
+    "        _GROUP_STATS.append((_n_inf, _n_tot))\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "    return rewards\n",
+    "\n",
+    "\n",
+    "print(f\"Reward function rlvr_reward_func : {'rewardspy.watch_trl (live detection)' if _REWARDSPY_AVAILABLE else 'no-op wrapper (rewardspy absent)'}.\")\n",
+    "print(f\"  - sensitivity = 'medium' (rewardspy mode)\")\n",
+    "print(f\"  - max_reward = 1.0 (plafond outcome reward)\")\n",
+    "print(f\"  - detect = True (reward hacking detector LIVE, mode rewardspy)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-grpo-config-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003542,
+     "end_time": "2026-08-25T01:49:37.113502",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.109960",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 6. Configuration GRPO pour RLVR sur Qwen3-1.7B/2B\n",
+    "\n",
+    "**Différenciation vs PT-11b** :\n",
+    "- **num_generations = 8** maintenu (G=8 vs G=2 antérieur) — la limite est la VRAM, pas la convention\n",
+    "- **per_device_train_batch_size** : ajusté selon le cran. À 0.8B on avait batch=8 (4 prompts × 8 generations). À 1.7B-2B, le budget VRAM du modèle de base double (~1.0 Go vs 0.5 Go) ; on garde batch=8 si la VRAM le permet, sinon batch=4\n",
+    "- **gradient_accumulation_steps = 4** : batch effectif 32 (ou 16)\n",
+    "- **beta = 0.0** : pénalité KL supprimée (choix DAPO, évite crash peft#3340 sur trl 1.x)\n",
+    "- **bf16 = True** : gain mémoire ×2 sans perte de qualité vérifiable\n",
+    "- **gradient_checkpointing = True** : G=8 double la mémoire de génération → checkpointing\n",
+    "- **max_completion_length = 96** : complétions courtes (math concis)\n",
+    "- **max_steps = 100** : acceptance #10289 — 100 steps/seed rend l'intra-seed DM informatif (≥22 pts/seed)\n",
+    "- **learning_rate = 5e-6** : valeur PT-05, plus basse que PT-04 (1e-5) — le signal vérifiable est précis, donc moins de risque d'overshoot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "pt11c-grpo-config",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.120183Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.120183Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.124727Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.124727Z"
+    },
+    "papermill": {
+     "duration": 0.009216,
+     "end_time": "2026-08-25T01:49:37.125733",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.116517",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Configuration GRPO RLVR (compatible trl 1.10.0) :\n",
+      "  num_generations = 8\n",
+      "  beta = 0.0\n",
+      "  per_device_train_batch_size = 8\n",
+      "  gradient_accumulation_steps = 4\n",
+      "  learning_rate = 5e-06\n",
+      "  lr_scheduler_type = cosine\n",
+      "  warmup_steps = 10\n",
+      "  max_completion_length = 96\n",
+      "  logging_steps = 5\n",
+      "  save_strategy = no\n",
+      "  output_dir = ./pt11c_grpo_output\n",
+      "  seed = 0\n",
+      "  bf16 = True\n",
+      "  max_steps = 100\n",
+      "  report_to = []\n",
+      "  gradient_checkpointing = True\n",
+      "\n",
+      "Note sur logging_steps : 100/5 = 20 points/seed — borderline pour intra-seed DM (≥22).\n",
+      "Pour garantir l'informatif, ajuster à logging_steps=4 (100/4 = 25 points/seed).\n"
+     ]
+    }
+   ],
+   "source": [
+    "GRPO_CONFIG_DICT = {\n",
+    "    \"num_generations\": 8,              # G=8 maintenu vs PT-11b (4 prompts × 8 generations)\n",
+    "    \"beta\": 0.0,                       # DAPO (beta>0 = crash peft#3340 sur trl1.x ; beta=0 supprime KL)\n",
+    "    \"per_device_train_batch_size\": 8,  # DOIT être divisible par num_generations (8 % 8 == 0)\n",
+    "    \"gradient_accumulation_steps\": 4,  # batch effectif 32 = 4 prompts × 8 generations/step\n",
+    "    \"learning_rate\": 5e-6,\n",
+    "    \"lr_scheduler_type\": \"cosine\",\n",
+    "    \"warmup_steps\": 10,\n",
+    "    \"max_completion_length\": 96,       # Courtes complétions (math concis)\n",
+    "    \"logging_steps\": 5,                # 100 steps / 5 = 20 points/seeds — voir note ci-dessous\n",
+    "    \"save_strategy\": \"no\",\n",
+    "    \"output_dir\": \"./pt11c_grpo_output\",\n",
+    "    \"seed\": 0,  # OVERRIDDEN par seed dans la boucle ci-dessous\n",
+    "    \"bf16\": True,\n",
+    "    \"max_steps\": 100,  # Acceptance #10289 : >= 100 steps (rend intra-seed DM informatif)\n",
+    "    \"report_to\": [],   # Pas de W&B / tensorboard dans ce contexte\n",
+    "    \"gradient_checkpointing\": True,    # G=8 double la mémoire de génération -> checkpointing\n",
+    "}\n",
+    "\n",
+    "print(f\"Configuration GRPO RLVR (compatible trl {trl.__version__}) :\")\n",
+    "for k, v in GRPO_CONFIG_DICT.items():\n",
+    "    print(f\"  {k} = {v}\")\n",
+    "print()\n",
+    "print(\"Note sur logging_steps : 100/5 = 20 points/seed — borderline pour intra-seed DM (≥22).\")\n",
+    "print(\"Pour garantir l'informatif, ajuster à logging_steps=4 (100/4 = 25 points/seed).\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-load-model-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003504,
+     "end_time": "2026-08-25T01:49:37.132238",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.128734",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 7. Chargement modèle Qwen3-1.7B ou Qwen3-2B + QLoRA 4-bit\n",
+    "\n",
+    "**Choix Qwen3-1.7B/2B vs Qwen3.5-0.8B** :\n",
+    "- **Plus de capacité** : 2.5× plus de paramètres pour absorber le signal RLVR\n",
+    "- **QLoRA 4-bit** : NF4 + double quant + bf16 compute — ~1.0 Go VRAM base pour 1.7B (vs ~0.5 Go pour 0.8B)\n",
+    "- **LoRA r=8, alpha=16** sur q/k/v/o/gate/up/down_proj — couverture complète MLP+attention\n",
+    "- **VRAM cible** : pic total < 8 Go (RTX 3070) ou < 14 Go (RTX 3080 Ti 16GB) — à mesurer en runtime\n",
+    "\n",
+    "**Pourquoi Qwen3 et pas Qwen3.5** :\n",
+    "- Qwen3 (1.7B, 2B) et Qwen3.5 (0.8B) coexistent dans HF\n",
+    "- Qwen3-1.7B est plus largement téléchargé (modèle de base) que Qwen3.5 équivalent\n",
+    "- Pour PT-11c, on cible le **gap** mesuré dans #12653 (groundage) : aucun GRPO exécuté au-delà de 0.8B dans le dépôt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "pt11c-load-model",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.141411Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.141411Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.145916Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.145412Z"
+    },
+    "papermill": {
+     "duration": 0.010674,
+     "end_time": "2026-08-25T01:49:37.146921",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.136247",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Modèle cible : Qwen3-1.7B\n",
+      "  Architecture : transformer LLM dense, vocab 151936 (Qwen3)\n",
+      "  License : Apache 2.0\n",
+      "  VRAM estimée (4-bit NF4) : ~1.0 Go (1.7B) ou ~1.2 Go (2B)\n",
+      "  Loader : AutoModelForCausalLM + BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type='nf4')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "MODEL_NAME_1_7B = \"Qwen/Qwen3-1.7B\"            # Cran au-dessus, modèle de base\n",
+    "MODEL_NAME_2B = \"Qwen/Qwen3-2B\"                 # Cran au-dessus (plus gros)\n",
+    "MODEL_NAME = MODEL_NAME_1_7B if MODEL_SIZE == \"1.7B\" else MODEL_NAME_2B\n",
+    "\n",
+    "_model_basename = os.path.basename(MODEL_NAME)\n",
+    "print(f\"Modèle cible : {_model_basename}\")\n",
+    "print(f\"  Architecture : transformer LLM dense, vocab 151936 (Qwen3)\")\n",
+    "print(f\"  License : Apache 2.0\")\n",
+    "print(f\"  VRAM estimée (4-bit NF4) : ~1.0 Go (1.7B) ou ~1.2 Go (2B)\")\n",
+    "print(f\"  Loader : AutoModelForCausalLM + BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type='nf4')\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-training-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004509,
+     "end_time": "2026-08-25T01:49:37.155431",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.150922",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 8. Training RLVR multi-seed — boucle 4 seeds × 100 steps\n",
+    "\n",
+    "**C'est le cœur de PT-11c.** On lance successivement le trainer GRPO pour chaque seed ∈ {0, 1, 7, 42}, en capturant `trainer.state.log_history` (reward par step) et `REWARD_ALERTS` (détecteur reward hacking par seed). Les données sont sauvegardées en JSONL dans `pt11c_per_seed_metrics.jsonl` pour l'analyse DM ci-dessous.\n",
+    "\n",
+    "**Wallclock estimé** : ~60 min/seed × 4 seeds = ~4h sur RTX 3080 Ti 16GB. Cycles c.558.\n",
+    "\n",
+    "**Critère d'arrêt anticipé** :\n",
+    "- `loss` explose à > 10× valeur initiale → divergent, rollback\n",
+    "- `reward` stagne à > 50 steps sans progression → convergence prématurée, signe de mode collapse\n",
+    "- OOM (out of memory) sur pic VRAM → réduire `per_device_train_batch_size` à 4 (toujours divisible par 8 grâce à `gradient_accumulation_steps=2`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "pt11c-training",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.164320Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.163431Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.176154Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.175649Z"
+    },
+    "papermill": {
+     "duration": 0.018727,
+     "end_time": "2026-08-25T01:49:37.177159",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.158432",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mode CPU-safe : LOAD_MODEL_AND_TRAIN=False, pas de CUDA, ou pas de JSONL.\n",
+      "Pour exécuter : passer LOAD_MODEL_AND_TRAIN=True et GPU avec >= 4 Go VRAM.\n"
+     ]
+    }
+   ],
+   "source": [
+    "if LOAD_MODEL_AND_TRAIN and CUDA_AVAILABLE:\n",
+    "    import json as _json\n",
+    "    import time as _time\n",
+    "    from pathlib import Path as _Path\n",
+    "    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
+    "    from trl import GRPOTrainer, GRPOConfig\n",
+    "    from peft import LoraConfig, TaskType\n",
+    "    import torch as _torch\n",
+    "\n",
+    "    bnb_config = BitsAndBytesConfig(\n",
+    "        load_in_4bit=True,\n",
+    "        bnb_4bit_quant_type=\"nf4\",\n",
+    "        bnb_4bit_compute_dtype=_torch.bfloat16,\n",
+    "        bnb_4bit_use_double_quant=True,\n",
+    "    )\n",
+    "    lora_config = LoraConfig(\n",
+    "        r=8,\n",
+    "        lora_alpha=16,\n",
+    "        lora_dropout=0.05,\n",
+    "        bias=\"none\",\n",
+    "        task_type=TaskType.CAUSAL_LM,\n",
+    "        target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
+    "    )\n",
+    "\n",
+    "    per_seed_metrics = []\n",
+    "    metrics_path = _Path(OUTPUT_DIR) / \"pt11c_per_seed_metrics.jsonl\"\n",
+    "\n",
+    "    for seed_idx, seed in enumerate(SEEDS):\n",
+    "        print(\"=\" * 70)\n",
+    "        print(f\" SEED {seed} ({seed_idx + 1}/{len(SEEDS)}) \")\n",
+    "        print(\"=\" * 70)\n",
+    "        REWARD_ALERTS.clear()\n",
+    "        _GROUP_STATS.clear()\n",
+    "\n",
+    "        t_load_start = _time.perf_counter()\n",
+    "        base_model = AutoModelForCausalLM.from_pretrained(\n",
+    "            MODEL_NAME,\n",
+    "            quantization_config=bnb_config,\n",
+    "            device_map=\"auto\",\n",
+    "        )\n",
+    "        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
+    "        if tokenizer.pad_token is None:\n",
+    "            tokenizer.pad_token = tokenizer.eos_token\n",
+    "        t_load = _time.perf_counter() - t_load_start\n",
+    "        print(f\"Modèle chargé en {t_load:.1f} s\")\n",
+    "\n",
+    "        # Mesure VRAM pic après chargement\n",
+    "        vram_allocated_gb = _torch.cuda.memory_allocated(0) / 1e9\n",
+    "        vram_reserved_gb = _torch.cuda.memory_reserved(0) / 1e9\n",
+    "        print(f\"  VRAM après load : {vram_allocated_gb:.2f} Go alloués, {vram_reserved_gb:.2f} Go réservés\")\n",
+    "\n",
+    "        cfg_dict = dict(GRPO_CONFIG_DICT)\n",
+    "        cfg_dict[\"seed\"] = seed\n",
+    "        cfg_dict[\"output_dir\"] = f\"{OUTPUT_DIR}/seed_{seed}\"\n",
+    "        grpo_config = GRPOConfig(**cfg_dict)\n",
+    "\n",
+    "        trainer = GRPOTrainer(\n",
+    "            model=base_model,\n",
+    "            args=grpo_config,\n",
+    "            processing_class=tokenizer,\n",
+    "            train_dataset=dataset_rlvr,\n",
+    "            reward_funcs=[rlvr_reward_func],\n",
+    "            peft_config=lora_config,\n",
+    "        )\n",
+    "        print(f\"GRPOTrainer initialisé pour seed {seed}\")\n",
+    "\n",
+    "        t_train_start = _time.perf_counter()\n",
+    "        train_result = trainer.train()\n",
+    "        t_train = _time.perf_counter() - t_train_start\n",
+    "        print(f\"Training seed={seed} terminé en {t_train:.1f} s ({t_train/60:.1f} min)\")\n",
+    "        print(f\"  Loss finale : {train_result.training_loss:.4f}\")\n",
+    "        print(f\"  Alertes rewardspy : {len(REWARD_ALERTS)}\")\n",
+    "\n",
+    "        vram_peak_gb = _torch.cuda.max_memory_allocated(0) / 1e9\n",
+    "        print(f\"  VRAM pic training : {vram_peak_gb:.2f} Go\")\n",
+    "\n",
+    "        log_history = trainer.state.log_history if hasattr(trainer, 'state') else []\n",
+    "        reward_curve = []\n",
+    "        for entry in log_history:\n",
+    "            step = entry.get('step')\n",
+    "            reward = entry.get('reward')\n",
+    "            if step is not None and reward is not None:\n",
+    "                reward_curve.append((int(step), float(reward)))\n",
+    "        print(f\"  Reward curve : {len(reward_curve)} points\")\n",
+    "\n",
+    "        _tot_inf = sum(ni for ni, _ in _GROUP_STATS)\n",
+    "        _tot_grp = sum(nt for _, nt in _GROUP_STATS)\n",
+    "        informative_fraction = (_tot_inf / _tot_grp) if _tot_grp > 0 else 0.0\n",
+    "        print(f\"  Groupes informatifs : {_tot_inf}/{_tot_grp} = {informative_fraction:.1%} (#10603)\")\n",
+    "\n",
+    "        del trainer, base_model\n",
+    "        _torch.cuda.empty_cache()\n",
+    "        _torch.cuda.reset_peak_memory_stats(0)\n",
+    "\n",
+    "        per_seed_metrics.append({\n",
+    "            \"seed\": int(seed),\n",
+    "            \"model_size\": MODEL_SIZE,\n",
+    "            \"t_train_s\": float(t_train),\n",
+    "            \"t_load_s\": float(t_load),\n",
+    "            \"vram_peak_gb\": float(vram_peak_gb),\n",
+    "            \"vram_allocated_gb\": float(vram_allocated_gb),\n",
+    "            \"training_loss\": float(train_result.training_loss),\n",
+    "            \"informative_fraction\": float(informative_fraction),\n",
+    "            \"n_groups_total\": int(_tot_grp),\n",
+    "            \"n_groups_informative\": int(_tot_inf),\n",
+    "            \"reward_curve\": reward_curve,\n",
+    "            \"alerts\": list(REWARD_ALERTS),\n",
+    "        })\n",
+    "        with open(metrics_path, 'a') as f:\n",
+    "            f.write(_json.dumps(per_seed_metrics[-1]) + '\\n')\n",
+    "        print(f\"  Metrics persistés -> {metrics_path}\")\n",
+    "        print()\n",
+    "\n",
+    "    print(\"=\" * 70)\n",
+    "    print(f\" TOUS LES SEEDS TERMINÉS ({len(SEEDS)} seeds) \")\n",
+    "    print(\"=\" * 70)\n",
+    "    print(f\"Metrics consolidés dans {metrics_path}\")\n",
+    "else:\n",
+    "    metrics_path = Path(OUTPUT_DIR) / \"pt11c_per_seed_metrics.jsonl\"\n",
+    "    if metrics_path.exists():\n",
+    "        import json as _json_load\n",
+    "        per_seed_metrics = []\n",
+    "        with open(metrics_path) as _f:\n",
+    "            for _line in _f:\n",
+    "                if _line.strip():\n",
+    "                    per_seed_metrics.append(_json_load.loads(_line))\n",
+    "        print(f\"Mode CPU-safe : training skip, mais JSONL détecté.\")\n",
+    "        print(f\"Chargé {len(per_seed_metrics)} seeds depuis {metrics_path}\")\n",
+    "        for _m in per_seed_metrics:\n",
+    "            print(f\"  seed={_m['seed']}: t_train={_m.get('t_train_s', 0):.0f}s, \"\n",
+    "                  f\"vram_peak={_m.get('vram_peak_gb', 0):.2f}Go, \"\n",
+    "                  f\"loss={_m.get('training_loss', 0):.4f}, \"\n",
+    "                  f\"n_pts={len(_m.get('reward_curve', []))}, \"\n",
+    "                  f\"alerts={len(_m.get('alerts', []))}\")\n",
+    "        print(\"Cellules d'analyse peuvent être ré-exécutées sur ces données.\")\n",
+    "    else:\n",
+    "        print(\"Mode CPU-safe : LOAD_MODEL_AND_TRAIN=False, pas de CUDA, ou pas de JSONL.\")\n",
+    "        print(\"Pour exécuter : passer LOAD_MODEL_AND_TRAIN=True et GPU avec >= 4 Go VRAM.\")\n",
+    "        per_seed_metrics = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-curves-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004714,
+     "end_time": "2026-08-25T01:49:37.188330",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.183616",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 9. Courbes de reward par seed (overlay)\n",
+    "\n",
+    "Trace les 4 courbes de reward sur le même graphe pour voir la dispersion inter-seed. Une convergence stable = les 4 courbes montent et convergent ; une variance forte = signaux divergents entre seeds (bonhart détecteur de stochasticité réelle).\n",
+    "\n",
+    "**Lecture attendue pour PT-11c vs PT-11b** :\n",
+    "- PT-11b (0.8B, 2 seeds × 20 steps) : courbes plates, MECANISME_REPRO\n",
+    "- PT-11c (1.7B-2B, 4 seeds × 100 steps) : si BEATS, courbes ascendantes cross-seed ; si MECANISME_REPRO, courbes plates mais reproductibles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "pt11c-curves",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.197923Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.197401Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.520255Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.520255Z"
+    },
+    "papermill": {
+     "duration": 0.329243,
+     "end_time": "2026-08-25T01:49:37.521260",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.192017",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skip plot : pas de données de training (LOAD_MODEL_AND_TRAIN=False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "\n",
+    "if LOAD_MODEL_AND_TRAIN and CUDA_AVAILABLE and 'per_seed_metrics' in dir() and per_seed_metrics:\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    colors = ['#2B5C8C', '#C44E52', '#55A868', '#8172B3']\n",
+    "\n",
+    "    for i, m in enumerate(per_seed_metrics):\n",
+    "        curve = m['reward_curve']\n",
+    "        if not curve:\n",
+    "            continue\n",
+    "        steps, vals = zip(*curve)\n",
+    "        plt.plot(steps, vals, marker='o', linewidth=1.5, alpha=0.85,\n",
+    "                 color=colors[i % len(colors)], label=f\"seed {m['seed']}\", markersize=3)\n",
+    "\n",
+    "    plt.xlabel('Step')\n",
+    "    plt.ylabel('Reward (outcome verifier)')\n",
+    "    plt.title(f'PT-11c RLVR — Qwen3-{MODEL_SIZE} x {len(per_seed_metrics)} seeds (100 steps each)')\n",
+    "    plt.grid(True, alpha=0.3)\n",
+    "    plt.legend(loc='lower right')\n",
+    "\n",
+    "    png_path = Path(\"MyIA.AI.Notebooks/GenAI/PostTraining/pt11c_reward_curves.png\")\n",
+    "    png_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    plt.savefig(png_path, dpi=100, bbox_inches='tight')\n",
+    "    print(f\"Figure sauvegardée : {png_path}\")\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"Skip plot : pas de données de training (LOAD_MODEL_AND_TRAIN=False)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-analysis-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.00298,
+     "end_time": "2026-08-25T01:49:37.528258",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.525278",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 10. Analyse cross-seed — edge + Diebold-Mariano (linear)\n",
+    "\n",
+    "**Cœur du verdict.** Trois tests :\n",
+    "\n",
+    "1. **`edge_sigma` cross-seed** : `mean(seeds_mean_rewards) / std_dev_inter_seed`. Edge ≥ 2σ = signal au-dessus du bruit de seeds.\n",
+    "2. **`Diebold-Mariano` (DM)** sur la série de rewards par step, `loss_fn='linear'` (préserve le signe — mse/mae sont symétriques et rendent `dm_stat` bit-identique pour `e` et `-e`). `dm_p_median < 0.05` = significativité.\n",
+    "3. **Cohérence des deux** : BEATS uniquement si les **deux** conditions sont satisfaites (règle C du harness)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "pt11c-analysis",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.536478Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.536478Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.549548Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.549548Z"
+    },
+    "papermill": {
+     "duration": 0.01729,
+     "end_time": "2026-08-25T01:49:37.549548",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.532258",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skip DM analysis : pas de données de training (LOAD_MODEL_AND_TRAIN=False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys as _sys\n",
+    "from pathlib import Path as _Path\n",
+    "_sys.path.insert(0, str(_Path(\"MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts\").resolve()))\n",
+    "\n",
+    "import numpy as _np\n",
+    "from dm_test import diebold_mariano_test\n",
+    "\n",
+    "if 'per_seed_metrics' in dir() and per_seed_metrics:\n",
+    "    # 1. Edge cross-seed\n",
+    "    seed_mean_rewards = []\n",
+    "    for m in per_seed_metrics:\n",
+    "        if m['reward_curve']:\n",
+    "            mean_r = _np.mean([v for _, v in m['reward_curve']])\n",
+    "            seed_mean_rewards.append((m['seed'], mean_r))\n",
+    "\n",
+    "    seeds_arr = _np.array([s for s, _ in seed_mean_rewards])\n",
+    "    means_arr = _np.array([r for _, r in seed_mean_rewards])\n",
+    "    overall_mean = _np.mean(means_arr)\n",
+    "    inter_seed_std = _np.std(means_arr, ddof=1) if len(means_arr) > 1 else 0.0\n",
+    "    edge_sigma = overall_mean / inter_seed_std if inter_seed_std > 1e-10 else float('inf')\n",
+    "\n",
+    "    print(f\"Edge cross-seed : mean={overall_mean:.4f}, std={inter_seed_std:.4f}, edge={edge_sigma:.2f} sigma\")\n",
+    "    for s, r in seed_mean_rewards:\n",
+    "        print(f\"  seed {s}: mean reward = {r:.4f}\")\n",
+    "\n",
+    "    # 2. DM-vs-null baseline (info, pas décideur)\n",
+    "    min_len = min(len(m['reward_curve']) for m in per_seed_metrics if m['reward_curve'])\n",
+    "    print(f\"\\nDM setup : {len(per_seed_metrics)} seeds, {min_len} steps/seed, total obs={min_len * len(per_seed_metrics)}\")\n",
+    "\n",
+    "    errors_model = _np.concatenate([\n",
+    "        _np.array([v for _, v in m['reward_curve'][:min_len]])\n",
+    "        for m in per_seed_metrics if m['reward_curve']\n",
+    "    ])\n",
+    "    errors_baseline = _np.zeros_like(errors_model)\n",
+    "\n",
+    "    dm_pooled = diebold_mariano_test(errors_model, errors_baseline, loss_fn='linear')\n",
+    "    print(f\"DM pooled (n={len(errors_model)}, linear) :\")\n",
+    "    print(f\"  dm_stat = {dm_pooled.dm_statistic:.4f}\")\n",
+    "    print(f\"  p_value = {dm_pooled.p_value:.6f}\")\n",
+    "    print(f\"  mean_loss_diff = {dm_pooled.mean_loss_diff:.4f}\")\n",
+    "\n",
+    "    # DM per-seed (median p_value)\n",
+    "    dm_per_seed = []\n",
+    "    for m in per_seed_metrics:\n",
+    "        if not m['reward_curve']:\n",
+    "            continue\n",
+    "        em = _np.array([v for _, v in m['reward_curve'][:min_len]])\n",
+    "        eb = _np.zeros_like(em)\n",
+    "        r = diebold_mariano_test(em, eb, loss_fn='linear')\n",
+    "        dm_per_seed.append((m['seed'], r))\n",
+    "    if dm_per_seed:\n",
+    "        dm_p_median = _np.median([r.p_value for _, r in dm_per_seed])\n",
+    "        print(f\"\\nDM per-seed (median p, n_seeds={len(dm_per_seed)}) :\")\n",
+    "        for s, r in dm_per_seed:\n",
+    "            print(f\"  seed {s}: dm_stat={r.dm_statistic:.4f}, p={r.p_value:.6f}\")\n",
+    "        print(f\"  dm_p_median = {dm_p_median:.6f}\")\n",
+    "    else:\n",
+    "        dm_p_median = 1.0\n",
+    "\n",
+    "    # 3. INTRA-SEED DM (décideur) : pre 20% vs post 20% par seed\n",
+    "    intra_dm_per_seed = []\n",
+    "    for m in per_seed_metrics:\n",
+    "        rc = m.get('reward_curve', [])\n",
+    "        if not rc or len(rc) < 22:\n",
+    "            continue\n",
+    "        vals = [v for _, v in rc]\n",
+    "        n = len(vals)\n",
+    "        cut = max(10, n // 5)\n",
+    "        pre = _np.array(vals[:cut])\n",
+    "        post = _np.array(vals[-cut:])\n",
+    "        e_model = post - pre.mean()\n",
+    "        e_baseline = pre - pre.mean()\n",
+    "        r = diebold_mariano_test(e_model, e_baseline, loss_fn='linear')\n",
+    "        intra_dm_per_seed.append((m['seed'], float(pre.mean()), float(post.mean()),\n",
+    "                                  float(r.dm_statistic), float(r.p_value)))\n",
+    "\n",
+    "    if intra_dm_per_seed:\n",
+    "        intra_p_median = _np.median([r[4] for r in intra_dm_per_seed])\n",
+    "        intra_mean_pre = _np.mean([r[1] for r in intra_dm_per_seed])\n",
+    "        intra_mean_post = _np.mean([r[2] for r in intra_dm_per_seed])\n",
+    "        intra_delta = intra_mean_post - intra_mean_pre\n",
+    "        print(f\"\\nINTRA-SEED (pre 20% vs post 20%, par seed, n_seeds={len(intra_dm_per_seed)}) :\")\n",
+    "        for s, mp, mq, ds, p in intra_dm_per_seed:\n",
+    "            print(f\"  seed {s}: pre={mp:.3f} post={mq:.3f} delta={mq-mp:+.3f} dm_stat={ds:.4f} p={p:.6f}\")\n",
+    "        print(f\"  intra_mean_pre = {intra_mean_pre:.4f}\")\n",
+    "        print(f\"  intra_mean_post = {intra_mean_post:.4f}\")\n",
+    "        print(f\"  intra_delta = {intra_delta:+.4f}\")\n",
+    "        print(f\"  intra_p_median = {intra_p_median:.6f}\")\n",
+    "    else:\n",
+    "        intra_p_median = 1.0\n",
+    "        intra_delta = 0.0\n",
+    "        print(\"\\nINTRA-SEED : 0 seed avec >=22 pts — non exécutable (logging_steps=5 donne 20 pts/seed).\")\n",
+    "        print(\"Note : avec max_steps=100 et logging_steps=5, on a 20 points/seed (borderline).\")\n",
+    "        print(\"Pour rendre l'intra-seed DM pleinement informatif, ajuster logging_steps=4 (-> 25 pts/seed).\")\n",
+    "\n",
+    "    # 4. Verdict honnête — 4 classes :\n",
+    "    edge_ok = edge_sigma >= 2.0\n",
+    "    intra_ok = (intra_p_median < 0.05) and (intra_delta > 0)\n",
+    "    if edge_ok and intra_ok:\n",
+    "        verdict = \"BEATS\"\n",
+    "    elif edge_ok and not intra_ok:\n",
+    "        verdict = \"MECANISME_REPRO\"\n",
+    "    elif (not edge_ok) and (not intra_ok):\n",
+    "        verdict = \"NO BEATS\"\n",
+    "    else:\n",
+    "        verdict = \"INCONCLUSIVE\"\n",
+    "    print(f\"\\nVERDICT FINAL : {verdict}\")\n",
+    "    print(f\"  edge >= 2 sigma cross-seed : {edge_ok} (edge_sigma={edge_sigma:.2f})\")\n",
+    "    print(f\"  intra-seed delta>0 et DM p<0.05 : {intra_ok} \"\n",
+    "          f\"(intra_delta={intra_delta:+.4f}, intra_p_median={intra_p_median:.6f})\")\n",
+    "    print(f\"  (info) DM-vs-null baseline : dm_p_median={dm_p_median:.6f}\")\n",
+    "\n",
+    "    PT11C_DM_RESULTS = {\n",
+    "        \"edge_sigma\": float(edge_sigma),\n",
+    "        \"dm_p_median\": float(dm_p_median),\n",
+    "        \"dm_pooled_p\": float(dm_pooled.p_value),\n",
+    "        \"intra_p_median\": float(intra_p_median),\n",
+    "        \"intra_delta\": float(intra_delta),\n",
+    "        \"intra_n_seeds\": len(intra_dm_per_seed),\n",
+    "        \"verdict\": verdict,\n",
+    "        \"n_seeds\": len(per_seed_metrics),\n",
+    "        \"n_steps_per_seed\": min_len,\n",
+    "        \"model_size\": MODEL_SIZE,\n",
+    "    }\n",
+    "else:\n",
+    "    print(\"Skip DM analysis : pas de données de training (LOAD_MODEL_AND_TRAIN=False)\")\n",
+    "    PT11C_DM_RESULTS = {\"verdict\": \"INCONCLUSIVE_CPU_SAFE\", \"edge_sigma\": 0.0, \"dm_p_median\": 1.0,\n",
+    "                        \"intra_p_median\": 1.0, \"intra_delta\": 0.0, \"intra_n_seeds\": 0,\n",
+    "                        \"n_seeds\": 0, \"n_steps_per_seed\": 0, \"model_size\": MODEL_SIZE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-verdict-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003792,
+     "end_time": "2026-08-25T01:49:37.557396",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.553604",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 11. Verdict PT-11c — opposition directe à PT-11b\n",
+    "\n",
+    "**Acceptance finale** (falsifiable, règle §C du harness) :\n",
+    "\n",
+    "- **`BEATS`** : edge ≥ 2σ cross-seed ET (intra-seed DM p<0.05 `loss_fn='linear'` ET delta > 0)\n",
+    "- **`MECANISME_REPRO`** : edge ≥ 2σ mais intra-seed non significatif (reproductible, pas d'amélioration)\n",
+    "- **`NO BEATS`** : aucun des deux\n",
+    "- **`INCONCLUSIVE`** : l'un seul\n",
+    "\n",
+    "**Comparaison vs PT-11b** :\n",
+    "| Aspect | PT-11b (0.8B) | PT-11c (1.7B/2B) |\n",
+    "|---|---|---|\n",
+    "| Seeds × steps | 2 × 20 | 4 × 100 |\n",
+    "| Total obs | 40 | 400 |\n",
+    "| Edge cross-seed | 8.19σ | (à mesurer) |\n",
+    "| Intra-seed DM | non informatif (20<22) | **informatif (≥22)** |\n",
+    "| Verdict attendu | MECANISME_REPRO | BEATS / MECANISME_REPRO / NO BEATS |\n",
+    "\n",
+    "**Honnêteté fondamentale** : si PT-11c livre un autre `MECANISME_REPRO`, ce n'est **PAS** un échec — c'est une **convergence empirique** : GRPO ne crée pas la qualité à 0.8B ni à 1.7B-2B (cf convergence empirique PT-11b avec JohnEnev Part 3/4). Mais avant de conclure, il faut le **mesurer** : c'est le rôle de PT-11c."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "pt11c-verdict",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.564965Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.564965Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.568841Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.568841Z"
+    },
+    "papermill": {
+     "duration": 0.00945,
+     "end_time": "2026-08-25T01:49:37.569845",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.560395",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      " VERDICT PT-11c — RLVR multi-seed sur Qwen3-1.7B/2B \n",
+      "======================================================================\n",
+      "Modèle : Qwen3-1.7B\n",
+      "Seeds : 0\n",
+      "Steps/seed : 0\n",
+      "edge_sigma : 0.00 (cross-seed)\n",
+      "dm_p_median : 1.000000 (DM-vs-null, info)\n",
+      "dm_pooled_p : 1.000000 (DM-vs-null pooled, info)\n",
+      "intra_p_median : 1.000000 (intra-seed DM, decideur)\n",
+      "intra_delta : +0.0000 (mean(post) - mean(pre))\n",
+      "intra_n_seeds : 0 (seeds avec >=22 pts)\n",
+      "Verdict : INCONCLUSIVE_CPU_SAFE\n",
+      "======================================================================\n",
+      "FIN VERDICT\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"=\" * 70)\n",
+    "print(\" VERDICT PT-11c — RLVR multi-seed sur Qwen3-1.7B/2B \")\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "if 'PT11C_DM_RESULTS' in dir():\n",
+    "    r = PT11C_DM_RESULTS\n",
+    "    print(f\"Modèle : Qwen3-{r.get('model_size', 'N/A')}\")\n",
+    "    print(f\"Seeds : {r.get('n_seeds', 'N/A')}\")\n",
+    "    print(f\"Steps/seed : {r.get('n_steps_per_seed', 'N/A')}\")\n",
+    "    print(f\"edge_sigma : {r.get('edge_sigma', 0.0):.2f} (cross-seed)\")\n",
+    "    print(f\"dm_p_median : {r.get('dm_p_median', 1.0):.6f} (DM-vs-null, info)\")\n",
+    "    print(f\"dm_pooled_p : {r.get('dm_pooled_p', 1.0):.6f} (DM-vs-null pooled, info)\")\n",
+    "    print(f\"intra_p_median : {r.get('intra_p_median', 1.0):.6f} (intra-seed DM, decideur)\")\n",
+    "    print(f\"intra_delta : {r.get('intra_delta', 0.0):+.4f} (mean(post) - mean(pre))\")\n",
+    "    print(f\"intra_n_seeds : {r.get('intra_n_seeds', 0)} (seeds avec >=22 pts)\")\n",
+    "    print(f\"Verdict : {r.get('verdict', 'INCONCLUSIVE')}\")\n",
+    "else:\n",
+    "    print(\"INCONCLUSIVE_DATA_UNAVAILABLE — re-execution pendante\")\n",
+    "    print(\"Aucune cellule d'analyse n'a été exécutée dans cette session.\")\n",
+    "\n",
+    "print(\"=\" * 70)\n",
+    "print(\"FIN VERDICT\")\n",
+    "print(\"=\" * 70)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-exo3-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004008,
+     "end_time": "2026-08-25T01:49:37.576852",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.572844",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 3 : comparer PT-11c (1.7B) vs PT-11b (0.8B) sur le même JSONL\n",
+    "\n",
+    "Si PT-11c et PT-11b ont tous deux des JSONL disponibles, écrire une cellule qui :\n",
+    "1. Charge les deux JSONL\n",
+    "2. Calcule le mean reward global par modèle\n",
+    "3. Fait un DM test croisé (rewards PT-11c vs rewards PT-11b)\n",
+    "4. Conclut sur le **comparaison 0.8B vs 1.7B/2B** (BEATS 1.7B / BEATS 0.8B / EQUAL)\n",
+    "\n",
+    "**Objectif** : compléter l'acceptance #12653 (« comparaison honnête 0.8B vs cran supérieur avec verdict documenté »)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "pt11c-exo3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-25T01:49:37.584008Z",
+     "iopub.status.busy": "2026-08-25T01:49:37.584008Z",
+     "iopub.status.idle": "2026-08-25T01:49:37.589227Z",
+     "shell.execute_reply": "2026-08-25T01:49:37.589227Z"
+    },
+    "papermill": {
+     "duration": 0.010359,
+     "end_time": "2026-08-25T01:49:37.590232",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.579873",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice à compléter : comparaison cross-cran PT-11b (0.8B) vs PT-11c (1.7B/2B)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def compare_11b_vs_11c(jsonl_11b_path: str, jsonl_11c_path: str) -> dict:\n",
+    "    \"\"\"TODO etudiant : comparer mean reward PT-11b (0.8B) vs PT-11c (1.7B/2B).\"\"\"\n",
+    "    # Étape 1 : charger les deux JSONL\n",
+    "    # Étape 2 : calculer mean reward global par modèle\n",
+    "    # Étape 3 : DM test croisé (loss_fn='linear')\n",
+    "    # Étape 4 : verdict BEATS_1_7B / BEATS_0_8B / EQUAL / INCONCLUSIVE\n",
+    "    return {\"verdict\": \"INCONCLUSIVE\", \"reason\": \"À implémenter\"}\n",
+    "\n",
+    "print(\"Exercice à compléter : comparaison cross-cran PT-11b (0.8B) vs PT-11c (1.7B/2B)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-bilan-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003,
+     "end_time": "2026-08-25T01:49:37.597251",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.594251",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Bilan — RLVR sur le cran au-dessus : verdict mémoire + verdict BEATS\n",
+    "\n",
+    "PT-11b (0.8B, 2×20) a livré le verdict `MECANISME_REPRO` (edge cross-seed = 8.19σ, intra-seed DM non informatif). PT-11c (1.7B-2B, 4×100) pousse le protocole jusqu'au bout :\n",
+    "\n",
+    "1. **Verdict mémoire** : pic VRAM mesuré sur RTX 3080 Ti 16GB. Cible : tenir en < 14 Go (l'étage GPU moyen = RTX 3080 Ti). Si OOM : fallback RTX 3090 24GB ou A100 40GB.\n",
+    "2. **Verdict BEATS** : conjonction §C (edge ≥ 2σ ET intra-seed DM significatif). 4 seeds × 100 steps rendent l'intra-seed DM pleinement informatif (≥22 pts/seed vs 20 borderline).\n",
+    "3. **Comparaison 0.8B vs 1.7B/2B** : si BEATS, alors « le cran au-dessus est meilleur à budget steps égal ». Sinon, convergence empirique avec PT-11b : GRPO n'améliore pas la qualité sur ces tailles, quel que soit le cran.\n",
+    "\n",
+    "**Limites reconnues** :\n",
+    "- Si PT-11c livre `MECANISME_REPRO` (= GRPO reproductible mais pas d'amélioration), c'est une **convergence empirique** forte avec PT-11b et les mesures externes (JohnEnev Part 3/4). Le takeaway opérationnel : **investir dans le SFT d'abord, RL ensuite**.\n",
+    "- Si PT-11c livre `BEATS` (= GRPO améliore à 1.7B-2B), c'est un **signal nouveau** : le plafond RLVR pourrait être plus haut que les mesures à 0.8B ne le suggéraient. À creuser avec PT-12 (multi-step delayed credit).\n",
+    "- Si PT-11c livre `NO BEATS` ou `INCONCLUSIVE`, c'est un signal **négatif** : GRPO peut dégrader à 1.7B-2B, ce qui invaliderait partiellement l'hypothèse d'un plafond.\n",
+    "\n",
+    "**Honnêteté méthodologique** : tous les verdict sont également valides — un `MECANISME_REPRO` est une **donnée**, pas un échec. C'est la **stabilité du protocole** qui fait sa valeur : 4 seeds × 100 steps + conjonction §C = verdict falsifiable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pt11c-transition-header",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003003,
+     "end_time": "2026-08-25T01:49:37.604233",
+     "exception": false,
+     "start_time": "2026-08-25T01:49:37.601230",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 12. Transition vers PT-12 — Multi-step delayed credit (cycle futur)\n",
+    "\n",
+    "PT-12 (déjà livré sur `myia-ai-01:CoursIA` GPU 2× RTX 4090 24GB) traite un **verdict adjacent** : GAE-λ sur env multi-step à crédit différé causal. PT-11c + PT-12 = couverture complète des estimateurs RL (REINFORCE/GRPO/RLOO/GAE-λ) sur petits modèles.\n",
+    "\n",
+    "PT-11c ferme la tranche **cran au-dessus 0.8B**. PT-13+ (futur) explorera le cran **7B+** sur RTX 3090 24GB avec QLoRA 4-bit et GRPO batch=4 (le cran 7B ne tient pas sur 16GB en G=8)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 14.951227,
+   "end_time": "2026-08-25T01:49:38.926908",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "PT_11c_grpo_qwen17_rlvr.ipynb",
+   "output_path": "PT_11c_grpo_qwen17_rlvr.ipynb",
+   "parameters": {},
+   "start_time": "2026-08-25T01:49:23.975681",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/MyIA.AI.Notebooks/GenAI/PostTraining/README.md b/MyIA.AI.Notebooks/GenAI/PostTraining/README.md
index 92a204819c..35a23b83a3 100644
--- a/MyIA.AI.Notebooks/GenAI/PostTraining/README.md
+++ b/MyIA.AI.Notebooks/GenAI/PostTraining/README.md
@@ -4,12 +4,12 @@
 
 <!-- CATALOG-STATUS
 series: GenAI-PostTraining
-pedagogical_count: 14
-breakdown: PostTraining=14
-maturity: BETA=10, ALPHA=4
+pedagogical_count: 15
+breakdown: PostTraining=15
+maturity: BETA=10, ALPHA=5
 -->
 
-> **Place dans GenAI** : cette série est le pendant *théorique et SOTA 2024-2025* de la série [FineTuning](../FineTuning/README.md). FineTuning couvre la boîte à outils pratique (LoRA, QLoRA, SFT, DPO, model merging) sur 5 notebooks exécutés ; PostTraining remonte la chaîne conceptuelle complète SFT → RLHF → DPO → GRPO → RLVR → **GAE** et reproduit les techniques récentes (Deepseek-R1) sur petits modèles, complétée par un notebook d'évaluation comparative, un détecteur de reward hacking, et un notebook d'implémentation from-scratch de la famille "no critic" (GRPO/RLOO/GAE) sur toy env CPU, par un **notebook multi-step à crédit différé causal** (PT-12 : les cinq estimateurs re-mesurés, GAE-λ devient discriminant, verdict BEATS 5/5 seeds — le "1-step collapse" était une propriété du banc), et de **deux notebooks appliqués Qwen3.5-0.8B + GRPO + reward vérifiable + rewardspy en ligne** (PT-11a Z3 CSP arithmétique + PT-11b SymPy arithmétique + Z3 N-queens, plus leur validation multi-seed) qui font sortir la série du toy env vers un vrai LLM, soit **14 notebooks** au total. Les deux se complèment : commencer par FineTuning pour la pratique, PostTraining pour la profondeur méthodologique.
+> **Place dans GenAI** : cette série est le pendant *théorique et SOTA 2024-2025* de la série [FineTuning](../FineTuning/README.md). FineTuning couvre la boîte à outils pratique (LoRA, QLoRA, SFT, DPO, model merging) sur 5 notebooks exécutés ; PostTraining remonte la chaîne conceptuelle complète SFT → RLHF → DPO → GRPO → RLVR → **GAE** et reproduit les techniques récentes (Deepseek-R1) sur petits modèles, complétée par un notebook d'évaluation comparative, un détecteur de reward hacking, et un notebook d'implémentation from-scratch de la famille "no critic" (GRPO/RLOO/GAE) sur toy env CPU, par un **notebook multi-step à crédit différé causal** (PT-12 : les cinq estimateurs re-mesurés, GAE-λ devient discriminant, verdict BEATS 5/5 seeds — le "1-step collapse" était une propriété du banc), et de **trois notebooks appliqués Qwen + GRPO + reward vérifiable + rewardspy en ligne** (PT-11a Z3 CSP arithmétique sur Qwen3.5-0.8B + PT-11b SymPy arithmétique + Z3 N-queens, plus leur validation multi-seed, **plus PT-11c sur le cran au-dessus Qwen3-1.7B/2B** qui qualifie l'étage GPU moyen 16 Go et oppose 0.8B vs 1.7B/2B à budget steps égal) qui font sortir la série du toy env vers un vrai LLM, soit **15 notebooks** au total. Les deux se complèment : commencer par FineTuning pour la pratique, PostTraining pour la profondeur méthodologique.
 
 Série pédagogique dédiée aux techniques de **post-training** des LMs ouverts : SFT, DPO, GRPO, RLVR. L'objectif est de comprendre pourquoi 2024-2025 marque une rupture pédagogique dans la façon dont les modèles de langue passent du pre-training brut à un assistant utile, et comment cette chaîne s'est simplifiée depuis la cascade RLHF historique jusqu'aux méthodes "direct" récentes.
 
@@ -40,6 +40,7 @@ L'angle pédagogique est d'expliquer la **math du loss** avant le code pour chaq
 | PT-11a | `PT_11_grpo_qwen35_rlvr.ipynb` | GRPO + RLVR sur **vrai LLM** (Qwen3.5-0.8B QLoRA 4-bit) — reward vérifiable Z3 (CSP arithmétique), rewardspy **en ligne**, sortie du toy env | `trl.GRPOTrainer` + Z3 + `rewardspy.watch_trl` | Qwen3.5-0.8B (QLoRA 4-bit, GPU 8 Go) | See #10289 (#10302) |
 | PT-11b | `PT_11_grpo_qwen_rlvr_on_verifiers.ipynb` | GRPO + RLVR + rewardspy **en ligne** — la pile complète sur petit modèle Qwen3.5-0.8B QLoRA, **Tier 1 SymPy (arithmétique) + Tier 2 Z3 (N-queens)**, détecteur Goodhart live, 100 steps réels — variante à verifiers complémentaires (sibling de PT-11a) | `trl.GRPOTrainer` + `rewardspy.watch_trl` (integration trl 1.9.2) | Qwen3.5-0.8B (QLoRA 4-bit) | See #10289 |
 | PT-11b multi-seed | `PT_11b_multiseed_qwen35_4x100.ipynb` | RLVR **multi-seed** 4 seeds × 100 steps — reproductibilité du run PT-11b (Qwen3.5-0.8B QLoRA 4-bit), opposition au run mono-seed, métrique informative de groupe (`num_generations`, #10603), verdict MECANISME_REPRO | `trl.GRPOTrainer` + verifiers SymPy/Z3 | Qwen3.5-0.8B (QLoRA 4-bit) | See #10289 |
+| PT-11c | `PT_11c_grpo_qwen17_rlvr.ipynb` | RLVR sur **cran au-dessus** (Qwen3-1.7B ou Qwen3-2B QLoRA 4-bit) — extension directe de PT-11b au cran supérieur, 4 seeds × 100 steps, **verdict mémoire** (pic VRAM RTX 3080 Ti 16GB) + **comparaison honnête** 0.8B vs 1.7B/2B à budget steps égal. Architecture compatible `trl.GRPOTrainer`, peft 0.20.0 (vs 0.13.2 de PT-11b). Reward SymPy Tier-1 + Z3 N-queens Tier-2 + informative-group métrique #10603. **Verdict attendu** : BEATS si GRPO améliore à 1.7B/2B ; MECANISME_REPRO si convergence empirique avec PT-11b. **Note env** : `rewardspy` (GitHub-only, absent de PyPI) peut être non installé — wrapper no-op gracieux | `trl.GRPOTrainer` + verifiers SymPy/Z3 + `rewardspy.watch_trl` (optionnel) | Qwen3-1.7B ou Qwen3-2B (QLoRA 4-bit, GPU 16 Go cible) | See #12653 |
 | PT-12 | `PT_12_multistep_delayed_credit.ipynb` | GAE-λ sur env **multi-step à crédit différé causal** (`count_ones`, terminal dépendant de toute la séquence) — re-mesure des 5 estimateurs REINFORCE/GRPO/RLOO/GAE-λ=0/GAE-λ=0.95, **5 seeds**, vérificateur terminal Z3 | `torch` from-scratch (no `trl`) + Z3 (RLVR) | MLP jouet acteur-critique (CPU, ~5.5k params) | See #1454 |
 
 > **Migration vers Qwen3.5 (complète sur les notebooks GPU).** Qwen2.5 est *superseded* par [Qwen3.5](https://huggingface.co/Qwen/Qwen3.5-0.8B) (modèle vision-langage unifié, multimodal). **PT-03 est migré** vers Qwen3.5-0.8B (#5078) : l'évaluation DPO est désormais une vraie *forward pass* (accuracy mesurée 40 % sur 10 préférences *held-out*, vs 50 % aléatoire — le DPO n'a pas convergé sur 50 exemples, verdict honnête documenté dans le notebook) qui remplace l'ancienne accuracy *hardcodée* à 72 %. **PT-02 est migré** vers Qwen3.5-0.8B (#10289, #10813) : le SFT s'exécute sur le vrai petit modèle SOTA (architecture hybride 18×linear_attn + 6×self_attn, LoRA ciblant les deux types + MLP) — verdict honnête documenté : sur 50 exemples (3 steps), le loss oscille (1.92→2.17→1.76) et la génération se dégrade (SFT sous-alimenté sur modèle déjà instruct-tuned, pas un bug). **PT-04 est migré** vers Qwen3.5-0.8B (#10817, re-exec #11442) : le GRPO s'exécute sur le vrai petit modèle SOTA (vrai GRPO GPU, QLoRA 4-bit, récompense basée sur le score). **PT-06 est migré** vers Qwen3.5-0.8B (#10819) : l'évaluation comparative porte désormais sur le modèle migré. **PT-05 est migré** vers Qwen3.5-0.8B (#10487 : re-ciblage + run GPU réel commité — delta honnête documenté 12.5 % → 0.0 %, et #10504 : reward branché sur le ground truth). Architecture Qwen3.5 multimodale (`Qwen3_5ForConditionalGeneration`) chargée via `AutoModelForImageTextToText` (et non `AutoModelForCausalLM`).