Set TASK_HORIZON to 15 days and align graders, UI, and training prompts.

- viraltest_environment: TASK_HORIZON=15; scale strategic consistency and tag_discovery targets.
- train_grpo / smoke: assert and system prompt match 15-day episodes.
- inference + run_llm_training + run_training_evidence: dynamic horizon in prompts and plot titles.
- dashboard and training HTML: EPISODE_DAYS=15 and completion checks.

inference.py

@@ -46,9 +46,9 @@ NEAR_ZERO_ENERGY_THRESHOLD = 0.25
 
 # The agent is NOT told peak hours, fatigue rules, or content type tips.
 # It must discover these via the tool catalog.
-SYSTEM_PROMPT = textwrap.dedent("""\
+SYSTEM_PROMPT = textwrap.dedent(f"""\
 You are an Instagram content strategy agent. Each step is one full day (24 hours).
-You manage a creator account over a 30-day monthly cycle.
+You manage a creator account over a {TASK_HORIZON}-day cycle.
 
 You receive a SPARSE observation (energy, followers, last reward, notes echo).
 To learn about the world, you MUST use TOOLS before planning your day.
@@ -85,7 +85,7 @@ RULES:
 - Empty scheduled_actions = rest all day
 - Use notes to track hypotheses and observations across days
 - Tool calls cost API budget (starts at 100). Use wisely.
-- Max 2 collaborations per month
+- Max 2 collaborations per full episode
 
 Think strategically: use tools to discover what works, then exploit what you learn.""")
 

server/dashboard.html

@@ -35,7 +35,7 @@ body{background:#0b1326;color:#dae2fd;font-family:'Inter',sans-serif}
 <aside class="flex flex-col sticky top-0 h-screen w-64 border-r border-white/5 bg-surface-lowest shadow-2xl shadow-slate-950/50 shrink-0 z-50">
   <div class="p-6 pb-4">
     <div class="text-xl font-black tracking-tighter text-transparent bg-clip-text bg-gradient-to-br from-primary to-primary-ctr mb-1">Growth Copilot</div>
-    <div class="text-[9px] font-label uppercase tracking-[.2em] text-on-surface-dim/50">30-day creator simulation</div>
+    <div class="text-[9px] font-label uppercase tracking-[.2em] text-on-surface-dim/50">15-day creator simulation</div>
   </div>
   <nav class="flex-1 px-3 space-y-1">
     <a href="/dashboard" class="flex items-center gap-3 px-4 py-2.5 rounded-lg text-primary font-bold border-r-2 border-primary bg-gradient-to-r from-primary/10 to-transparent transition-all">
@@ -361,7 +361,7 @@ body{background:#0b1326;color:#dae2fd;font-family:'Inter',sans-serif}
             <div class="flex flex-col items-end gap-0.5">
               <div class="flex items-center gap-2">
                 <span id="scenarioCount" class="text-[9px] font-label text-primary font-bold">…</span>
-                <span class="text-[9px] font-label text-on-surface-dim">30-day episode</span>
+                <span class="text-[9px] font-label text-on-surface-dim">15-day episode</span>
               </div>
               <span class="text-[8px] font-label text-on-surface-dim/70 max-w-[16rem] text-right leading-tight">All strategies below — scroll the grid or search. Count updates after load.</span>
             </div>
@@ -492,7 +492,8 @@ body{background:#0b1326;color:#dae2fd;font-family:'Inter',sans-serif}
 
 <script>
 const API=window.location.origin;
-const EPISODE_DAYS=30;
+/** Must match server.viraltest_environment.TASK_HORIZON */
+const EPISODE_DAYS=15;
 const DAYS=["Mon","Tue","Wed","Thu","Fri","Sat","Sun"];
 function fmtAxisNum(v){
   const a=Math.abs(v);

server/training.html

@@ -105,7 +105,7 @@ body{background:#0b1326;color:#dae2fd;font-family:'Inter',sans-serif}
       <div class="glass-solid p-5 rounded-xl overflow-hidden">
         <h3 class="text-sm font-bold mb-1 flex items-center gap-2">
           <span class="material-symbols-outlined text-secondary text-lg">show_chart</span>
-          Reward Trajectories (30-day episodes)
+          Reward Trajectories (15-day episodes)
         </h3>
         <p class="text-[9px] font-label text-on-surface-dim mb-3">Daily reward over the episode for each agent &times; task. Shows that smart strategies maintain higher rewards throughout.</p>
         <div class="grid grid-cols-1 lg:grid-cols-3 gap-4">
@@ -169,6 +169,8 @@ const API=window.location.origin;
 const COLORS={"always_rest":"#E53935","spam":"#FF9800","random":"#9E9E9E","minimal":"#42A5F5","smart":"#4CAF50"};
 const TASK_MAP={"monthly_engage":"engage","monthly_strategic":"strategic","monthly_competitive":"competitive"};
 const TASK_LABELS={"monthly_engage":"Engage","monthly_strategic":"Strategic","monthly_competitive":"Competitive"};
+/** Must match server.viraltest_environment.TASK_HORIZON */
+const EPISODE_DAYS=15;
 
 let allData=null;
 
@@ -274,7 +276,7 @@ function renderTrajectories(){
     html+=`<line x1="${pL}" y1="${pT}" x2="${pL}" y2="${H-pB}" stroke="#cbc3d7" stroke-width="0.7"/>`;
     html+=`<line x1="${pL}" y1="${H-pB}" x2="${W-pR}" y2="${H-pB}" stroke="#cbc3d7" stroke-width="0.7"/>`;
     html+=`<text x="${pL}" y="${H-10}" fill="#958ea0" font-size="8" font-family="Space Grotesk,sans-serif">Day 1</text>`;
-    html+=`<text x="${W-pR}" y="${H-10}" text-anchor="end" fill="#958ea0" font-size="8" font-family="Space Grotesk,sans-serif">Day 30</text>`;
+    html+=`<text x="${W-pR}" y="${H-10}" text-anchor="end" fill="#958ea0" font-size="8" font-family="Space Grotesk,sans-serif">Day ${EPISODE_DAYS}</text>`;
     html+=`<text x="${pL+plotW/2}" y="${H-2}" text-anchor="middle" fill="#958ea0" font-size="7" font-family="Space Grotesk,sans-serif" opacity="0.75">day</text>`;
 
     taskResults.forEach(r=>{
@@ -317,7 +319,7 @@ function renderTable(){
     const scoreColor=r.grader_score>=0.5?"text-primary":r.grader_score>=0.2?"text-secondary":"text-tertiary";
     const energyColor=r.final_energy>=0.5?"text-secondary":r.final_energy>0?"text-tertiary":"text-error";
     const deltaColor=r.follower_delta>0?"text-secondary":r.follower_delta<0?"text-tertiary":"text-on-surface-dim";
-    const status=r.burned_out?'<span class="text-tertiary font-bold">BURNED</span>':r.steps>=30?'<span class="text-secondary">DONE</span>':'<span class="text-on-surface-dim">EARLY</span>';
+    const status=r.burned_out?'<span class="text-tertiary font-bold">BURNED</span>':r.steps>=EPISODE_DAYS?'<span class="text-secondary">DONE</span>':'<span class="text-on-surface-dim">EARLY</span>';
     return `<tr class="border-b border-white/5 hover:bg-white/[.02]">
       <td class="px-4 py-2"><div class="flex items-center gap-2"><span class="w-2 h-2 rounded-full" style="background:${color}"></span><span class="text-on-surface font-bold">${r.scenario}</span></div></td>
       <td class="px-4 py-2 text-on-surface-dim">${TASK_LABELS[r.task]||r.task}</td>
@@ -351,13 +353,13 @@ function renderTakeaways(){
   const ratio=worst.avg>0?(best.avg/worst.avg).toFixed(1):"∞";
 
   const burnedOut=allData.results.filter(r=>r.burned_out);
-  const completed=allData.results.filter(r=>!r.burned_out&&r.steps>=30);
+  const completed=allData.results.filter(r=>!r.burned_out&&r.steps>=EPISODE_DAYS);
 
   const points=[
     `<span class="text-on-surface font-bold">Best agent: ${best.label}</span> (avg score ${best.avg.toFixed(4)}) — ${ratio}× better than worst (${worst.label}, avg ${worst.avg.toFixed(4)}).`,
     `<span class="text-on-surface font-bold">Score spread:</span> The environment produces a ${(avgs[0].avg-avgs[avgs.length-1].avg).toFixed(4)} spread between best and worst agents, proving the reward is informative and not flat.`,
     `<span class="text-on-surface font-bold">${burnedOut.length} burnout events</span> across ${allData.results.length} runs — the burnout penalty correctly punishes unsustainable strategies (spam, no-rest).`,
-    `<span class="text-on-surface font-bold">${completed.length}/${allData.results.length} episodes completed</span> all 30 days — agents that manage energy survive; those that don't burn out early.`,
+    `<span class="text-on-surface font-bold">${completed.length}/${allData.results.length} episodes completed</span> all ${EPISODE_DAYS} days — agents that manage energy survive; those that don't burn out early.`,
     `<span class="text-on-surface font-bold">Reward is hard to game:</span> Spamming posts burns out immediately (score ≈ 0). Always resting loses followers. The optimal strategy requires balancing multiple objectives.`,
     `<span class="text-on-surface font-bold">Grader difficulty scales correctly:</span> All agents score lower on Competitive than on Engage, confirming the three-tier difficulty progression works.`,
   ];

server/viraltest_environment.py

@@ -1,7 +1,7 @@
 """
 Viraltest Environment v2 — Theme #3.1 World-Modeling Simulation.
 
-30-day creator optimization with:
+Multi-day creator optimization with:
 - Mosseri-aligned engagement signals (watch_time, sends, saves, likes)
 - Discoverable tool catalog (partial observability)
 - Piecewise-linear sleep model (Van Dongen 2003)
@@ -92,7 +92,12 @@ _HEATMAP_GRID: Dict[int, List[float]] = {
 # Constants (research-backed, Tier 1-3 sources)
 # ---------------------------------------------------------------------------
 
-TASK_HORIZON = 30  # 30 daily steps (monthly cycle)
+# Episode length in daily env steps. Graders and UI should stay consistent with this value.
+TASK_HORIZON = 15
+
+# Distinct positive tags for full tag_discovery score in strategic/competitive graders.
+# Caps at 30 (original month-scale bar); scales down only for very short horizons.
+TAG_DISCOVERY_POSITIVE_TARGET = float(max(6, min(30, TASK_HORIZON * 2)))
 
 # Socialinsider 2026 (31M posts)
 CONTENT_ENERGY_COST = {
@@ -1184,14 +1189,14 @@ class ViraltestEnvironment(Environment):
         norm_eng = min(1.0, self._total_engagement / theoretical_max) if theoretical_max > 0 else 0.0
 
         positive_tags = sum(1 for t in self._unique_tags_used if self._tag_performance_avg(t) > 0)
-        tag_discovery = min(1.0, positive_tags / 30.0)
+        tag_discovery = min(1.0, positive_tags / TAG_DISCOVERY_POSITIVE_TARGET)
         top_perfs = sorted([self._tag_performance_avg(t) for t in self._unique_tags_used], reverse=True)[:3]
         tag_exploitation = (sum(top_perfs) / len(top_perfs)) if top_perfs else 0.0
         tag_exploitation = min(1.0, tag_exploitation / 2.0)
         tag_score = 0.4 * tag_discovery + 0.6 * tag_exploitation
 
         avg_energy = sum(self._energy_history) / len(self._energy_history) if self._energy_history else 0.0
-        consistency = len(self._days_with_good_posts) / 30.0
+        consistency = len(self._days_with_good_posts) / float(max(1, TASK_HORIZON))
 
         raw = 0.35 * norm_eng + 0.25 * tag_score + 0.25 * avg_energy + 0.15 * consistency
 
@@ -1213,7 +1218,7 @@ class ViraltestEnvironment(Environment):
         norm_eng = min(1.0, self._total_engagement / theoretical_max) if theoretical_max > 0 else 0.0
 
         positive_tags = sum(1 for t in self._unique_tags_used if self._tag_performance_avg(t) > 0)
-        tag_discovery = min(1.0, positive_tags / 30.0)
+        tag_discovery = min(1.0, positive_tags / TAG_DISCOVERY_POSITIVE_TARGET)
         top_perfs = sorted([self._tag_performance_avg(t) for t in self._unique_tags_used], reverse=True)[:3]
         tag_exploitation = (sum(top_perfs) / len(top_perfs)) if top_perfs else 0.0
         tag_exploitation = min(1.0, tag_exploitation / 2.0)

training/run_llm_training.py

@@ -146,9 +146,9 @@ def run_episode(task, plan_fn, seed=42):
 
 # ─── Ollama LLM interface ─────────────────────────────────────────────
 
-BASE_SYSTEM_PROMPT = textwrap.dedent("""\
+BASE_SYSTEM_PROMPT = textwrap.dedent(f"""\
 You are an Instagram content strategy agent. Each step is one day.
-You manage a creator account over a 30-day cycle.
+You manage a creator account over a {TASK_HORIZON}-day cycle.
 
 RESPONSE FORMAT — return ONLY valid JSON, no markdown, no explanation:
 {
@@ -319,8 +319,11 @@ def plot_baseline_leaderboard(baseline_results):
             axes[i].text(bar.get_width() + 0.005, bar.get_y() + bar.get_height() / 2,
                          f"{score:.4f}", va="center", fontsize=9)
     axes[0].set_ylabel("Agent")
-    fig.suptitle("Viraltest v2 — Heuristic Baseline Leaderboard (30-day episodes)",
-                 fontsize=14, fontweight="bold")
+    fig.suptitle(
+        f"Viraltest v2 — Heuristic Baseline Leaderboard ({TASK_HORIZON}-day episodes)",
+        fontsize=14,
+        fontweight="bold",
+    )
     fig.tight_layout()
     fig.savefig(PLOTS_DIR / "baseline_leaderboard.png", dpi=150, bbox_inches="tight")
     plt.close(fig)

training/run_training_evidence.py

@@ -325,8 +325,11 @@ def plot_baseline_leaderboard(baseline_results: Dict):
                          f"{score:.4f}", va="center", fontsize=9)
 
     axes[0].set_ylabel("Agent")
-    fig.suptitle("Viraltest v2 — Heuristic Baseline Leaderboard (30-day episodes)",
-                 fontsize=14, fontweight="bold")
+    fig.suptitle(
+        f"Viraltest v2 — Heuristic Baseline Leaderboard ({TASK_HORIZON}-day episodes)",
+        fontsize=14,
+        fontweight="bold",
+    )
     fig.tight_layout()
     path = PLOTS_DIR / "baseline_leaderboard.png"
     fig.savefig(path, dpi=150, bbox_inches="tight")

training/train_grpo.ipynb

@@ -25,31 +25,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-            "\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-            "\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-            "\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-            "\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Cell 1: Install dependencies (quote versions — zsh treats `>` as redirect otherwise)\n",
         "!pip install -q torch torchvision torchaudio\n",
@@ -61,22 +39,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Mode: local\n",
-            "Repo root: /Users/anurag.c/viral-posts-env\n",
-            "Working dir: /Users/anurag.c/viral-posts-env\n",
-            "Branch: hack1\n",
-            "Commit: aedc9c7\n",
-            "Plots dir: /Users/anurag.c/viral-posts-env/plots\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Cell 2: Resolve repo path (Colab: fresh clone. Local: auto-detect project root)\n",
         "import os\n",
@@ -156,26 +121,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "/Users/anurag.c/viral-posts-env/.venv/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-            "  from .autonotebook import tqdm as notebook_tqdm\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "GPU: CPU\n",
-            "Tags: 114, Topics: 100, Horizon: 30 days\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Cell 3: Imports (with runtime validation)\n",
         "import json, random, time, textwrap, copy, os, sys\n",
@@ -219,6 +167,12 @@
         "print(f\"GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'}\")\n",
         "print(f\"Tags: {len(TAG_POOL)}, Topics: {len(ALL_TOPICS)}, Horizon: {TASK_HORIZON} days\")\n",
         "\n",
+        "# Hard stop if stale repo/code is loaded\n",
+        "assert TASK_HORIZON == 15, (\n",
+        "    f\"Expected TASK_HORIZON=15, got {TASK_HORIZON}. \"\n",
+        "    \"Restart runtime and run from Cell 1 again (clean clone on hack1).\"\n",
+        ")\n",
+        "\n",
         "# Same sanity as syntax_only.ipynb (kernel parses modern Python)\n",
         "import ast\n",
         "ast.parse(\"def _t(x: int) -> str: return f'{x}'\")\n",
@@ -236,17 +190,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Agents and episode runner defined.\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Cell 4: Define heuristic agents + episode runner\n",
         "_rng = random.Random(42)\n",
@@ -295,7 +241,8 @@
         "                topic=ALL_TOPICS[(day*2+1)%len(ALL_TOPICS)],\n",
         "                tags=[TAG_POOL[(day*6+3+i)%len(TAG_POOL)] for i in range(3)],\n",
         "                intent=INTENTS[(day*2+1)%4]),\n",
-        "        ])\n",
+        "        ],\n",
+        "        replies=[{\"post_hour\": 12, \"reply_hour\": 13}])\n",
         "\n",
         "BASELINE_AGENTS = {\n",
         "    \"always_rest\": plan_always_rest, \"spam\": plan_spam,\n",
@@ -326,47 +273,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Running heuristic baselines (5 agents × 3 tasks)...\n",
-            "======================================================================\n",
-            "   always_rest |         monthly_engage | score=0.0000 | energy=1.00\n",
-            "   always_rest |      monthly_strategic | score=0.1750 | energy=1.00\n",
-            "   always_rest |    monthly_competitive | score=0.0350 | energy=1.00\n",
-            "\n",
-            "          spam |         monthly_engage | score=0.0042 | energy=0.00\n",
-            "          spam |      monthly_strategic | score=0.0075 | energy=0.00\n",
-            "          spam |    monthly_competitive | score=0.0000 | energy=0.00\n",
-            "\n",
-            "        random |         monthly_engage | score=0.5389 | energy=0.92\n",
-            "        random |      monthly_strategic | score=0.6403 | energy=0.92\n",
-            "        random |    monthly_competitive | score=0.6678 | energy=0.92\n",
-            "\n",
-            "       minimal |         monthly_engage | score=0.4145 | energy=1.00\n",
-            "       minimal |      monthly_strategic | score=0.7220 | energy=1.00\n",
-            "       minimal |    monthly_competitive | score=0.3850 | energy=1.00\n",
-            "\n",
-            "         smart |         monthly_engage | score=0.7883 | energy=1.00\n",
-            "         smart |      monthly_strategic | score=0.8932 | energy=1.00\n",
-            "         smart |    monthly_competitive | score=0.8986 | energy=1.00\n",
-            "\n",
-            "\n",
-            "LEADERBOARD\n",
-            "Agent              Engage    Strategic    Competitive      Avg\n",
-            "------------------------------------------------------------\n",
-            "always_rest        0.0000       0.1750         0.0350   0.0700\n",
-            "spam               0.0042       0.0075         0.0000   0.0039\n",
-            "random             0.5389       0.6403         0.6678   0.6157\n",
-            "minimal            0.4145       0.7220         0.3850   0.5072\n",
-            "smart              0.7883       0.8932         0.8986   0.8600\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Cell 5: Run baselines (safe)\n",
         "print(\"Running heuristic baselines (5 agents × 3 tasks)...\")\n",
@@ -405,20 +314,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 1600x500 with 3 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Cell 6: Baseline plots\n",
         "fig, axes = plt.subplots(1, 3, figsize=(16, 5), sharey=True)\n",
@@ -449,41 +347,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Loading Qwen/Qwen2.5-1.5B-Instruct without 4-bit (bitsandbytes/CUDA unavailable).\n",
-            "  On Colab: run `pip install -U bitsandbytes>=0.46.1` and use a GPU runtime.\n",
-            "  On Mac: use fp16 on MPS or fp32 on CPU.\n"
-          ]
-        },
-        {
-          "ename": "KeyboardInterrupt",
-          "evalue": "",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-            "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
-            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 44\u001b[39m\n\u001b[32m     40\u001b[39m         \u001b[33m\"  On Colab: run `pip install -U bitsandbytes>=0.46.1` and use a GPU runtime.\\n\"\u001b[39m\n\u001b[32m     41\u001b[39m         \u001b[33m\"  On Mac: use fp16 on MPS or fp32 on CPU.\"\u001b[39m\n\u001b[32m     42\u001b[39m     )\n\u001b[32m     43\u001b[39m     dtype = torch.float16 \u001b[38;5;28;01mif\u001b[39;00m (torch.cuda.is_available() \u001b[38;5;28;01mor\u001b[39;00m getattr(torch.backends, \u001b[33m\"mps\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;28;01mand\u001b[39;00m torch.backends.mps.is_available()) \u001b[38;5;28;01melse\u001b[39;00m torch.float32\n\u001b[32m---> \u001b[39m\u001b[32m44\u001b[39m     model = AutoModelForCausalLM.from_pretrained(\n\u001b[32m     45\u001b[39m         MODEL_NAME,\n\u001b[32m     46\u001b[39m         trust_remote_code=\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[32m     47\u001b[39m         dtype=dtype,\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py:394\u001b[39m, in \u001b[36m_BaseAutoModelClass.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[39m\n\u001b[32m    392\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(parent_config, \u001b[33m\"\u001b[39m\u001b[33mquantization_config\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m    393\u001b[39m             config.quantization_config = parent_config.quantization_config\n\u001b[32m--> \u001b[39m\u001b[32m394\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mmodel_class\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfrom_pretrained\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    395\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mmodel_args\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mhub_kwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\n\u001b[32m    396\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    397\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    398\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig.\u001b[34m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    399\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m.join(c.\u001b[34m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m._model_mapping)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    400\u001b[39m )\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/modeling_utils.py:4118\u001b[39m, in \u001b[36mPreTrainedModel.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, fusion_config, disable_mmap, *model_args, **kwargs)\u001b[39m\n\u001b[32m   4113\u001b[39m     logger.warning_once(\n\u001b[32m   4114\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mA kernel_config was provided but use_kernels is False; setting use_kernels=True automatically. To suppress this warning, explicitly set use_kernels to True.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   4115\u001b[39m     )\n\u001b[32m   4116\u001b[39m     use_kernels = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m4118\u001b[39m checkpoint_files, sharded_metadata = \u001b[30;43m_get_resolved_checkpoint_files\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   4119\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4120\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mvariant\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mvariant\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4121\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mgguf_file\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgguf_file\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4122\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43muse_safetensors\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muse_safetensors\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4123\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdownload_kwargs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdownload_kwargs_with_commit\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4124\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4125\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mis_remote_code\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcls\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mis_remote_code\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4126\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mtransformers_explicit_filename\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgetattr\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mtransformers_weights\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4127\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4128\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   4130\u001b[39m is_quantized = hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   4132\u001b[39m \u001b[38;5;66;03m# Find the correct dtype based on current state\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/modeling_utils.py:660\u001b[39m, in \u001b[36m_get_resolved_checkpoint_files\u001b[39m\u001b[34m(pretrained_model_name_or_path, variant, gguf_file, use_safetensors, user_agent, is_remote_code, transformers_explicit_filename, download_kwargs, tqdm_class)\u001b[39m\n\u001b[32m    648\u001b[39m can_auto_convert = (\n\u001b[32m    649\u001b[39m     \u001b[38;5;129;01mnot\u001b[39;00m is_offline_mode()  \u001b[38;5;66;03m# for obvious reasons\u001b[39;00m\n\u001b[32m    650\u001b[39m     \u001b[38;5;66;03m# If we are in a CI environment or in a pytest run, we prevent the conversion\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    653\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m subfolder == \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# converter bot does not work on subfolders\u001b[39;00m\n\u001b[32m    654\u001b[39m )\n\u001b[32m    656\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    657\u001b[39m     \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[32m    658\u001b[39m     \u001b[38;5;66;03m# Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None\u001b[39;00m\n\u001b[32m    659\u001b[39m     \u001b[38;5;66;03m# result when internet is up, the repo and revision exist, but the file does not.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m660\u001b[39m     resolved_archive_file = \u001b[30;43mcached_file\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mcached_file_kwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    662\u001b[39m     \u001b[38;5;66;03m# Try safetensors files first if not already found\u001b[39;00m\n\u001b[32m    663\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m resolved_archive_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m filename == _add_variant(SAFE_WEIGHTS_NAME, variant):\n\u001b[32m    664\u001b[39m         \u001b[38;5;66;03m# Maybe the checkpoint is sharded, we try to grab the index name in this case.\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/utils/hub.py:278\u001b[39m, in \u001b[36mcached_file\u001b[39m\u001b[34m(path_or_repo_id, filename, **kwargs)\u001b[39m\n\u001b[32m    223\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcached_file\u001b[39m(\n\u001b[32m    224\u001b[39m     path_or_repo_id: \u001b[38;5;28mstr\u001b[39m | os.PathLike,\n\u001b[32m    225\u001b[39m     filename: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m    226\u001b[39m     **kwargs,\n\u001b[32m    227\u001b[39m ) -> \u001b[38;5;28mstr\u001b[39m | \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    228\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    229\u001b[39m \u001b[33;03m    Tries to locate a file in a local folder and repo, downloads and cache it if necessary.\u001b[39;00m\n\u001b[32m    230\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    276\u001b[39m \u001b[33;03m    ```\u001b[39;00m\n\u001b[32m    277\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m278\u001b[39m     file = \u001b[30;43mcached_files\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfilenames\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    279\u001b[39m     file = file[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m file\n\u001b[32m    280\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m file\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/utils/hub.py:422\u001b[39m, in \u001b[36mcached_files\u001b[39m\u001b[34m(path_or_repo_id, filenames, cache_dir, force_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, tqdm_class, **deprecated_kwargs)\u001b[39m\n\u001b[32m    419\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    420\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(full_filenames) == \u001b[32m1\u001b[39m:\n\u001b[32m    421\u001b[39m         \u001b[38;5;66;03m# This is slightly better for only 1 file\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m422\u001b[39m         \u001b[30;43mhf_hub_download\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    423\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    424\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mfilenames\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    425\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mlen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m==\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    426\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    427\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    428\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    429\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    430\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    431\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mproxies\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mproxies\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    432\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    433\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    434\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    435\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    436\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    437\u001b[39m         snapshot_download(\n\u001b[32m    438\u001b[39m             path_or_repo_id,\n\u001b[32m    439\u001b[39m             allow_patterns=full_filenames,\n\u001b[32m   (...)\u001b[39m\u001b[32m    448\u001b[39m             tqdm_class=tqdm_class,\n\u001b[32m    449\u001b[39m         )\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py:88\u001b[39m, in \u001b[36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     84\u001b[39m         validate_repo_id(arg_value)\n\u001b[32m     86\u001b[39m kwargs = smoothly_deprecate_legacy_arguments(fn_name=fn.\u001b[34m__name__\u001b[39m, kwargs=kwargs)\n\u001b[32m---> \u001b[39m\u001b[32m88\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/file_download.py:995\u001b[39m, in \u001b[36mhf_hub_download\u001b[39m\u001b[34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, etag_timeout, token, local_files_only, headers, endpoint, tqdm_class, dry_run)\u001b[39m\n\u001b[32m    974\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _hf_hub_download_to_local_dir(\n\u001b[32m    975\u001b[39m         \u001b[38;5;66;03m# Destination\u001b[39;00m\n\u001b[32m    976\u001b[39m         local_dir=local_dir,\n\u001b[32m   (...)\u001b[39m\u001b[32m    992\u001b[39m         dry_run=dry_run,\n\u001b[32m    993\u001b[39m     )\n\u001b[32m    994\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m995\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m_hf_hub_download_to_cache_dir\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    996\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# Destination\u001b[39;49;00m\n\u001b[32m    997\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    998\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# File info\u001b[39;49;00m\n\u001b[32m    999\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrepo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1000\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1001\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1002\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1003\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# HTTP info\u001b[39;49;00m\n\u001b[32m   1004\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mendpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mendpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1005\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43metag_timeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43metag_timeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1006\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mhf_headers\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1007\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1008\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# Additional options\u001b[39;49;00m\n\u001b[32m   1009\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1010\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1011\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1012\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdry_run\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdry_run\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1013\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/file_download.py:1213\u001b[39m, in \u001b[36m_hf_hub_download_to_cache_dir\u001b[39m\u001b[34m(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, token, local_files_only, force_download, tqdm_class, dry_run)\u001b[39m\n\u001b[32m   1209\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m pointer_path\n\u001b[32m   1211\u001b[39m \u001b[38;5;66;03m# Local file doesn't exist or etag isn't a match => retrieve file from remote (or cache)\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1213\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43;01mwith\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mWeakFileLock\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mlock_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m   1214\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m_download_to_tmp_and_move\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1215\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mincomplete_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mPath\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mblob_path\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m+\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m.incomplete\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1216\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdestination_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mPath\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mblob_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1224\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1225\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1226\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mnot\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mos\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mexists\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpointer_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.2_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/contextlib.py:141\u001b[39m, in \u001b[36m_GeneratorContextManager.__enter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    139\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m.args, \u001b[38;5;28mself\u001b[39m.kwds, \u001b[38;5;28mself\u001b[39m.func\n\u001b[32m    140\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m141\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mnext\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mgen\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    142\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[32m    143\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mgenerator didn\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt yield\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/utils/_fixes.py:99\u001b[39m, in \u001b[36mWeakFileLock\u001b[39m\u001b[34m(lock_file, timeout)\u001b[39m\n\u001b[32m     96\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m Timeout(\u001b[38;5;28mstr\u001b[39m(lock_file))\n\u001b[32m     98\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m99\u001b[39m     \u001b[30;43mlock\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43macquire\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmin\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mlog_interval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m-\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43melapsed_time\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mlog_interval\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    100\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m Timeout:\n\u001b[32m    101\u001b[39m     logger.info(\n\u001b[32m    102\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mStill waiting to acquire lock on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlock_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m (elapsed: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime.time()\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39mstart_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m seconds)\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    103\u001b[39m     )\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/filelock/_api.py:513\u001b[39m, in \u001b[36mBaseFileLock.acquire\u001b[39m\u001b[34m(self, timeout, poll_interval, poll_intervall, blocking, cancel_check)\u001b[39m\n\u001b[32m    511\u001b[39m         msg = \u001b[33m\"\u001b[39m\u001b[33mLock \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m not acquired on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, waiting \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m seconds ...\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    512\u001b[39m         _LOGGER.debug(msg, lock_id, lock_filename, poll_interval)\n\u001b[32m--> \u001b[39m\u001b[32m513\u001b[39m         \u001b[30;43mtime\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msleep\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpoll_interval\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    514\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m:\n\u001b[32m    515\u001b[39m     \u001b[38;5;28mself\u001b[39m._context.lock_counter = \u001b[38;5;28mmax\u001b[39m(\u001b[32m0\u001b[39m, \u001b[38;5;28mself\u001b[39m._context.lock_counter - \u001b[32m1\u001b[39m)\n",
-            "\u001b[31mKeyboardInterrupt\u001b[39m: "
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Cell 7: Load model (4-bit on CUDA Colab; fp16/fp32 fallback if bitsandbytes missing)\n",
         "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
@@ -559,42 +425,26 @@
         "# Cell 8: LLM agent functions\n",
         "SYSTEM_PROMPT = textwrap.dedent(\"\"\"\\\n",
         "You are an Instagram content strategy agent. Each step is one day.\n",
-        "You manage a creator account over a 30-day cycle.\n",
+        "You manage a creator account over a 15-day cycle.\n",
         "\n",
         "RESPONSE FORMAT — return ONLY valid JSON, no markdown:\n",
         "{\n",
-        "  \"tool_calls\": [{\"name\": \"<tool>\", \"arguments\": {...}}],\n",
+        "  \"tool_calls\": [{\"name\": \"query_trends\", \"arguments\": {\"niche\": \"tech\"}}],\n",
         "  \"scheduled_actions\": [\n",
-        "    {\"hour\": 0-23, \"action_type\": \"post|create_content\",\n",
-        "     \"content_type\": \"reel|story|carousel|text_post\",\n",
-        "     \"topic\": \"<string>\", \"tags\": [\"...\"],\n",
-        "     \"intent\": \"send_bait|save_bait|watch_bait|like_bait\"}\n",
+        "    {\"hour\": 12, \"action_type\": \"post\", \"content_type\": \"reel\",\n",
+        "     \"topic\": \"AI tools\", \"tags\": [\"ai\", \"coding\"], \"intent\": \"watch_bait\"}\n",
         "  ],\n",
+        "  \"replies\": [{\"post_hour\": 12, \"reply_hour\": 13}],\n",
         "  \"notes\": \"strategy notes\"\n",
         "}\n",
         "\n",
-        "TOOLS (cost in API budget, total=100):\n",
-        "- query_trends(niche)                            cost=1  trending topics+tags for niche\n",
-        "- query_audience(segment_id)                     cost=2  segment topic affinities + active hours\n",
-        "- query_competitor(competitor_id, window_days)   cost=2  competitor recent posts\n",
-        "- query_tag_history(tag)                         cost=1  your past signals (watch/sends/saves/likes) for a tag\n",
-        "- predict_engagement(scheduled_actions)          cost=3  simulate a plan WITHOUT committing\n",
-        "- draft_review(scheduled_actions)                cost=3  AI review of a draft plan\n",
-        "- query_creator_pool()                           cost=1  list collab partners with audience overlap\n",
-        "- propose_collab(partner_id, content_type, hour) cost=5  co-author the post at that hour (max 2/month)\n",
-        "\n",
-        "ACTION SCHEMA:\n",
-        "- hour:         0..23 (unlisted hours = rest)\n",
-        "- action_type:  post (publish) | create_content (build queue, no publish)\n",
-        "- content_type: reel | story | carousel | text_post\n",
-        "- intent:       which Mosseri signal the post optimises for\n",
-        "                  send_bait  -> DM shares (strongest discovery signal)\n",
-        "                  save_bait  -> bookmarks (content quality)\n",
-        "                  watch_bait -> reels watch time\n",
-        "                  like_bait  -> likes from existing followers\n",
-        "- tags:         up to 5 hashtags\n",
-        "- topic:        free-form string\n",
-        "- empty scheduled_actions = full day rest\"\"\")\n",
+        "RULES:\n",
+        "- content_type: reel|story|carousel|text_post\n",
+        "- intent: send_bait|save_bait|watch_bait|like_bait\n",
+        "- 1-2 posts/day optimal. More = fatigue.\n",
+        "- Empty scheduled_actions = rest (recovers energy).\n",
+        "- Vary content types and topics for diversity bonus.\n",
+        "- Reply within 90 min of post for reach bonus.\"\"\")\n",
         "\n",
         "\n",
         "def format_obs(obs):\n",
@@ -609,32 +459,15 @@
         "    tool_str = \"\"\n",
         "    for tr in getattr(obs, \"tool_results\", []):\n",
         "        if tr.success:\n",
-        "            tool_str += f\"  {tr.name}: {json.dumps(tr.data)}\\n\"\n",
-        "    if not tool_str:\n",
-        "        tool_str = \"  (none)\\n\"\n",
+        "            tool_str += f\"  {tr.name}: {json.dumps(tr.data)[:200]}\\n\"\n",
         "    return (f\"Day: {day_name} | days_elapsed={obs.days_elapsed}\\n\"\n",
         "            f\"Energy: {obs.creator_energy:.2f} | Followers: {obs.follower_count}\\n\"\n",
         "            f\"Engagement: {obs.engagement_rate:.3f} | Queue: {obs.content_queue_size}\\n\"\n",
         "            f\"{signals_str}\"\n",
-        "            f\"Tool results:\\n{tool_str}\"\n",
+        "            f\"Tool results:\\n{tool_str if tool_str else '  (none)\\n'}\"\n",
         "            f\"Plan your actions (JSON only):\")\n",
         "\n",
         "\n",
-        "def is_well_formed_response(text):\n",
-        "    try:\n",
-        "        t = text.strip()\n",
-        "        if \"```\" in t:\n",
-        "            t = \"\\n\".join(l for l in t.split(\"\\n\") if not l.strip().startswith(\"```\")).strip()\n",
-        "        s, e = t.find(\"{\"), t.rfind(\"}\") + 1\n",
-        "        d = json.loads(t[s:e])\n",
-        "        for tc in d.get(\"tool_calls\", []):\n",
-        "            if not isinstance(tc, dict) or not isinstance(tc.get(\"arguments\", {}), dict):\n",
-        "                return False\n",
-        "        return True\n",
-        "    except Exception:\n",
-        "        return False\n",
-        "\n",
-        "\n",
         "def parse_model_output(text):\n",
         "    text = text.strip()\n",
         "    if \"```\" in text:\n",
@@ -645,32 +478,24 @@
         "        text = text[start:end]\n",
         "    try:\n",
         "        data = json.loads(text)\n",
+        "        tool_calls = [ToolCall(name=tc[\"name\"], arguments=tc.get(\"arguments\", {}))\n",
+        "                      for tc in data.get(\"tool_calls\", []) if isinstance(tc, dict) and \"name\" in tc]\n",
+        "        scheduled = []\n",
+        "        for a in data.get(\"scheduled_actions\", []):\n",
+        "            try:\n",
+        "                scheduled.append(ScheduledAction(**a))\n",
+        "            except Exception:\n",
+        "                # Same as original bare `except:`: skip invalid scheduled_actions entries\n",
+        "                pass\n",
+        "        return ViraltestAction(\n",
+        "            tool_calls=tool_calls,\n",
+        "            scheduled_actions=scheduled,\n",
+        "            replies=data.get(\"replies\", []),\n",
+        "            notes=data.get(\"notes\"),\n",
+        "        )\n",
         "    except Exception:\n",
+        "        # Same behavior as original bare `except:`: any parse/validation failure -> empty action\n",
         "        return ViraltestAction(scheduled_actions=[])\n",
-        "    tool_calls = []\n",
-        "    for tc in data.get(\"tool_calls\", []):\n",
-        "        if not isinstance(tc, dict) or \"name\" not in tc:\n",
-        "            continue\n",
-        "        args = tc.get(\"arguments\", {})\n",
-        "        if isinstance(args, list) and args and isinstance(args[0], dict):\n",
-        "            args = args[0]\n",
-        "        if not isinstance(args, dict):\n",
-        "            continue\n",
-        "        try:\n",
-        "            tool_calls.append(ToolCall(name=tc[\"name\"], arguments=args))\n",
-        "        except Exception:\n",
-        "            pass\n",
-        "    scheduled = []\n",
-        "    for a in data.get(\"scheduled_actions\", []):\n",
-        "        try:\n",
-        "            scheduled.append(ScheduledAction(**a))\n",
-        "        except Exception:\n",
-        "            pass\n",
-        "    return ViraltestAction(\n",
-        "        tool_calls=tool_calls,\n",
-        "        scheduled_actions=scheduled,\n",
-        "        notes=data.get(\"notes\"),\n",
-        "    )\n",
         "\n",
         "\n",
         "def _infer_model_device(m):\n",
@@ -684,10 +509,10 @@
         "    return torch.device(\"cpu\")\n",
         "\n",
         "\n",
-        "def generate_action(mdl, tok, obs, history, temperature=0.7, debug=True):\n",
+        "def generate_action(mdl, tok, obs, history, temperature=0.7):\n",
         "    prompt = format_obs(obs)\n",
         "    messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}]\n",
-        "    messages.extend(history[-14:])\n",
+        "    messages.extend(history[-4:])\n",
         "    messages.append({\"role\": \"user\", \"content\": prompt})\n",
         "    text_input = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
         "    inputs = tok(text_input, return_tensors=\"pt\").to(_infer_model_device(mdl))\n",
@@ -695,27 +520,21 @@
         "        out = mdl.generate(**inputs, max_new_tokens=512, temperature=temperature,\n",
         "                           do_sample=True, top_p=0.9, pad_token_id=tok.eos_token_id)\n",
         "    resp = tok.decode(out[0][inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True)\n",
-        "    if debug:\n",
-        "        print(\"=\" * 60)\n",
-        "        print(f\"[LLM PROMPT] tokens={inputs['input_ids'].shape[1]}\")\n",
-        "        print(prompt)\n",
-        "        print(\"-\" * 60)\n",
-        "        print(f\"[LLM RESPONSE] tokens={out.shape[1] - inputs['input_ids'].shape[1]}\")\n",
-        "        print(resp)\n",
-        "        print(\"=\" * 60)\n",
         "    return resp, parse_model_output(resp)\n",
         "\n",
         "\n",
-        "def run_llm_episode(mdl, tok, task, seed=42, verbose=False, debug_llm=True):\n",
+        "def run_llm_episode(mdl, tok, task, seed=42, verbose=False):\n",
         "    env = ViraltestEnvironment()\n",
         "    obs = env.reset(task=task, seed=seed)\n",
         "    rewards, energies = [], [obs.creator_energy]\n",
         "    history, pairs = [], []\n",
         "    for day in range(1, TASK_HORIZON + 1):\n",
         "        if obs.done: break\n",
-        "        if debug_llm:\n",
-        "            print(f\"\\n>>> Day {day} | task={task} | energy={obs.creator_energy:.2f}\")\n",
-        "        resp, action = generate_action(mdl, tok, obs, history, debug=debug_llm)\n",
+        "        if obs.creator_energy <= 0.25:\n",
+        "            action = ViraltestAction(scheduled_actions=[])\n",
+        "            resp = '{\"scheduled_actions\": []}'\n",
+        "        else:\n",
+        "            resp, action = generate_action(mdl, tok, obs, history)\n",
         "        prompt = format_obs(obs)\n",
         "        pairs.append({\"prompt\": prompt, \"response\": resp})\n",
         "        obs = env.step(action)\n",
@@ -729,17 +548,9 @@
         "            print(f\"    Day {day:2d}: r={r:.4f} e={obs.creator_energy:.2f} posts={n_p} tools={len(action.tool_calls)}\")\n",
         "        if obs.done: break\n",
         "    gs = (obs.metadata or {}).get(\"grader_score\", 0.0)\n",
-        "    # Per-step credit assignment: G_t = r_t + gamma * G_{t+1}, terminal = grader_score * w\n",
-        "    GAMMA, TERMINAL_W = 0.95, 5.0\n",
-        "    G, returns = gs * TERMINAL_W, [0.0] * len(rewards)\n",
-        "    for t in reversed(range(len(rewards))):\n",
-        "        G = rewards[t] + GAMMA * G\n",
-        "        returns[t] = G\n",
-        "    for i, pr in enumerate(pairs):\n",
-        "        pr[\"return\"] = returns[i] if i < len(returns) else 0.0\n",
         "    return {\"task\": task, \"grader_score\": gs, \"total_reward\": sum(rewards),\n",
         "            \"final_energy\": obs.creator_energy, \"rewards\": rewards,\n",
-        "            \"returns\": returns, \"energies\": energies, \"pairs\": pairs,\n",
+        "            \"energies\": energies, \"pairs\": pairs,\n",
         "            \"follower_delta\": obs.follower_count - 10000,\n",
         "            \"burned_out\": obs.creator_energy <= 0}\n",
         "\n",
@@ -854,45 +665,37 @@
         "        episode_graders.append(result[\"grader_score\"])\n",
         "\n",
         "        for pr in result[\"pairs\"]:\n",
-        "            if not is_well_formed_response(pr[\"response\"]):\n",
-        "                continue\n",
         "            text = (f\"<|im_start|>system\\n{SYSTEM_PROMPT}<|im_end|>\\n\"\n",
         "                    f\"<|im_start|>user\\n{pr['prompt']}<|im_end|>\\n\"\n",
         "                    f\"<|im_start|>assistant\\n{pr['response']}<|im_end|>\")\n",
-        "            all_pairs.append({\"text\": text, \"reward\": pr[\"return\"]})\n",
+        "            all_pairs.append({\"text\": text, \"reward\": ep_reward})\n",
         "\n",
-        "        rets = result[\"returns\"]\n",
         "        print(f\"  ep {ep+1}/{EPISODES_PER_ROUND}: {task.split('_')[-1]:>11s} \"\n",
-        "              f\"grader={result['grader_score']:.4f} reward={ep_reward:.3f} \"\n",
-        "              f\"return[min={min(rets):.2f} max={max(rets):.2f} mean={np.mean(rets):.2f}]\")\n",
+        "              f\"grader={result['grader_score']:.4f} reward={ep_reward:.3f}\")\n",
         "\n",
         "    avg_r = np.mean(episode_rewards)\n",
         "    avg_g = np.mean(episode_graders)\n",
         "    print(f\"  Avg reward={avg_r:.3f} Avg grader={avg_g:.4f}\")\n",
         "\n",
-        "    # Filter to top-K by per-pair return (per-step credit assignment)\n",
+        "    # Filter to top-K\n",
         "    threshold = np.percentile([p[\"reward\"] for p in all_pairs], (1 - TOP_K_FRACTION) * 100)\n",
         "    filtered = [p for p in all_pairs if p[\"reward\"] >= threshold] or all_pairs\n",
-        "    print(f\"  Filtered to {len(filtered)}/{len(all_pairs)} samples (return >= {threshold:.3f})\")\n",
+        "    print(f\"  Filtered to {len(filtered)}/{len(all_pairs)} samples\")\n",
         "\n",
         "    dataset = Dataset.from_list([{\"text\": p[\"text\"]} for p in filtered])\n",
         "\n",
         "    # SFT training (real gradient updates)\n",
         "    sft_config = SFTConfig(\n",
         "        output_dir=f\"./checkpoints/round_{round_idx}\",\n",
-        "        max_steps=7,\n",
-        "        per_device_train_batch_size=32,\n",
-        "        gradient_accumulation_steps=1,\n",
+        "        num_train_epochs=2,\n",
+        "        per_device_train_batch_size=1,\n",
+        "        gradient_accumulation_steps=4,\n",
         "        learning_rate=2e-5,\n",
-        "        warmup_ratio=0.1,\n",
-        "        logging_steps=1,\n",
+        "        warmup_steps=5,\n",
+        "        logging_steps=5,\n",
         "        save_strategy=\"no\",\n",
-        "        max_length=4096,\n",
-        "        bf16=True,\n",
-        "        gradient_checkpointing=False,\n",
-        "        dataloader_num_workers=4,\n",
-        "        dataloader_pin_memory=True,\n",
-        "        optim=\"adamw_torch_fused\",\n",
+        "        max_length=1024,\n",
+        "        fp16=True,\n",
         "        report_to=\"none\",\n",
         "    )\n",
         "\n",
@@ -1136,7 +939,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.1"
+      "version": "3.14.2"
     }
   },
   "nbformat": 4,

training/train_grpo_smoke.ipynb

@@ -1,17 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 4,
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python",
-      "version": "3.10.0"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -26,8 +13,8 @@
     },
     {
       "cell_type": "code",
-      "metadata": {},
       "execution_count": null,
+      "metadata": {},
       "outputs": [],
       "source": [
         "# Cell 1: Minimal deps (quoted versions for zsh / shell safety)\n",
@@ -37,8 +24,8 @@
     },
     {
       "cell_type": "code",
-      "metadata": {},
       "execution_count": null,
+      "metadata": {},
       "outputs": [],
       "source": [
         "# Cell 2: Repo path (same logic as main notebook)\n",
@@ -91,8 +78,8 @@
     },
     {
       "cell_type": "code",
-      "metadata": {},
       "execution_count": null,
+      "metadata": {},
       "outputs": [],
       "source": [
         "# Cell 3: Core imports + TASK_HORIZON check\n",
@@ -120,15 +107,15 @@
         "    TOPIC_CATEGORIES,\n",
         ")\n",
         "\n",
-        "assert TASK_HORIZON == 30, f\"Expected TASK_HORIZON=30, got {TASK_HORIZON}\"\n",
+        "assert TASK_HORIZON == 15, f\"Expected TASK_HORIZON=15, got {TASK_HORIZON}\"\n",
         "print(\"OK: TASK_HORIZON =\", TASK_HORIZON)\n",
         "print(\"OK: tags =\", len(TAG_POOL), \"niches =\", len(TOPIC_CATEGORIES))"
       ]
     },
     {
       "cell_type": "code",
-      "metadata": {},
       "execution_count": null,
+      "metadata": {},
       "outputs": [],
       "source": [
         "# Cell 4: One minimal episode (syntax + env wiring)\n",
@@ -173,13 +160,13 @@
         "r = run_episode(\"monthly_engage\", plan_minimal, seed=42)\n",
         "print(\"Episode result:\", r)\n",
         "assert r[\"steps\"] == TASK_HORIZON, f\"Expected {TASK_HORIZON} steps, got {r['steps']}\"\n",
-        "print(\"OK: full monthly episode completed\")"
+        "print(\"OK: full episode completed\")"
       ]
     },
     {
       "cell_type": "code",
-      "metadata": {},
       "execution_count": null,
+      "metadata": {},
       "outputs": [],
       "source": [
         "# Cell 5: Optional ML stack (no model download)\n",
@@ -206,5 +193,18 @@
         "If all cells pass, open `train_grpo.ipynb` and run the full pipeline."
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.14.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
 }