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 <!DOCTYPE html>
-<html>
 <head>
-<meta charset="utf-8">
 <title>GRPO SQL Optimizer</title>
 <style>
-  body { font-family: sans-serif; max-width: 900px; margin: 40px auto; padding: 0 20px; }
-  img  { max-width: 100%; }
-  table { border-collapse: collapse; width: 100%; }
-  th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }
-  th { background: #f4f4f4; }
-  code { background: #f4f4f4; padding: 2px 6px; border-radius: 3px; }
 </style>
 </head>
 <body>
-<h1>GRPO Training for SQL Query Optimization</h1>
-<h2>Overview</h2>
-<p>Fine-tuned <code>Qwen/Qwen2.5-0.5B-Instruct</code> using GRPO (Group Relative Policy Optimization)
-reinforcement learning to optimize SQL queries using a DuckDB execution environment.</p>
-<h2>Results</h2>
-<img src="grpo_results.png" alt="Training Curve"/>
-<h3>Training Progress</h3>
-<table>
-<tr><th>Metric</th><th>Value</th></tr>
-<tr><td>Start avg (ep1-10)</td><td>0.3090</td></tr>
-<tr><td>End avg (ep91-100)</td><td>0.5962</td></tr>
-<tr><td>Improvement</td><td>+93%</td></tr>
-</table>
-<h3>Final Evaluation</h3>
-<table>
-<tr><th>Task</th><th>Difficulty</th><th>Score</th></tr>
-<tr><td>task_1_basic_antipatterns</td><td>easy</td><td>0.7500 ✅</td></tr>
-<tr><td>task_2_correlated_subqueries</td><td>medium</td><td>0.8313 ✅</td></tr>
-<tr><td>task_3_wildcard_scan</td><td>medium-hard</td><td>0.9250 ✅</td></tr>
-<tr><td>task_4_implicit_join</td><td>hard</td><td>0.6438 ✅</td></tr>
-<tr><td>task_5_window_functions</td><td>expert</td><td>0.6250 ⚠️</td></tr>
-<tr><td><strong>Average</strong></td><td></td><td><strong>0.7550</strong></td></tr>
-</table>
-<p><strong>Baseline: 0.63 &nbsp;|&nbsp; Improvement: +0.1250 (+12.5%)</strong></p>
-<h2>Approach</h2>
-<h3>GRPO Training</h3>
-<ul>
-<li><strong>Algorithm:</strong> GRPO (Group Relative Policy Optimization)</li>
-<li><strong>Base Model:</strong> Qwen/Qwen2.5-0.5B-Instruct</li>
-<li><strong>Episodes:</strong> 100 × 4 completions per prompt</li>
-<li><strong>Hardware:</strong> Kaggle GPU T4 x2</li>
-</ul>
-<h3>Reward Function</h3>
-<ul>
-<li><code>execution_speedup</code>: How much faster the optimized query runs</li>
-<li><code>result_correctness</code>: Whether results are identical</li>
-<li><code>issue_detection</code>: Whether SQL anti-patterns were identified</li>
-<li><code>approval_correctness</code>: Whether approval flag is correct</li>
-<li><code>summary_quality</code>: Quality of the explanation</li>
-</ul>
-<h2>Key Findings</h2>
-<ol>
-<li><strong>Reward variance is critical</strong> — Early runs had flat 0.08 rewards. Fixing the prompt to include schema information created reward variance needed for GRPO to learn.</li>
-<li><strong>Prompt engineering matters</strong> — Telling the model to use only columns from the schema was the single most impactful fix.</li>
-<li><strong>Partial credit helps</strong> — Adding issue detection bonus gave the model a learning signal even when SQL execution failed.</li>
-</ol>
-<h2>Links</h2>
-<ul>
-<li><a href="https://huggingface.co/laterabhi/grpo-sql-optimizer">Model on HuggingFace</a></li>
-<li><a href="https://github.com/OfficialAbhinavSingh/SQL-Query-Optimization-Environment-">SQL Environment</a></li>
-<li><a href="https://arxiv.org/abs/2402.03300">GRPO Paper</a></li>
-</ul>
 </body>
 </html>

 <!DOCTYPE html>
+<html lang="en">
 <head>
+<meta charset="utf-8" />
+<meta name="viewport" content="width=device-width, initial-scale=1" />
 <title>GRPO SQL Optimizer</title>
 <style>
+  :root{
+    --bg:#0d1117; --fg:#e6edf3; --muted:#8b949e; --acc:#58a6ff;
+    --card:#161b22; --bd:#30363d; --ok:#3fb950; --warn:#d29922;
+  }
+  *{box-sizing:border-box}
+  body{
+    margin:0;
+    font-family: ui-sans-serif, system-ui, -apple-system, Segoe UI, Roboto, Arial, sans-serif;
+    background:var(--bg);
+    color:var(--fg);
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+  a{color:var(--acc); text-decoration:none}
+  a:hover{text-decoration:underline}
+  code{background:#1f2630; padding:2px 6px; border-radius:6px; border:1px solid var(--bd)}
+  .wrap{max-width:980px; margin:0 auto; padding:18px 18px 40px}
+  /* Top bar */
+  .hfTopbar{
+    position: sticky; top: 0; z-index: 9999;
+    display:flex; gap:10px; align-items:center; justify-content:space-between;
+    padding:10px 14px;
+    background: rgba(13,17,23,.92);
+    backdrop-filter: blur(8px);
+    border-bottom: 1px solid rgba(48,54,61,.9);
+  }
+  .hfTopbar .left{display:flex; gap:10px; align-items:center; flex-wrap:wrap}
+  .hfTopbar .brand{font-weight:800; color:var(--fg)}
+  .hfTopbar .pill{
+    color:var(--fg); text-decoration:none;
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+    padding:6px 10px;
+    border-radius:999px;
+    font-size:12px;
+    display:inline-flex; align-items:center; gap:6px;
+  }
+  .hfTopbar .pill:hover{background:#1f2630; text-decoration:none}
+  .hfTopbar .muted{color:var(--muted); font-size:12px}
+  /* Content */
+  h1{font-size:28px; margin:18px 0 8px}
+  h2{font-size:18px; margin:22px 0 10px; color:var(--acc)}
+  p{color:var(--fg); line-height:1.6}
+  .sub{color:var(--muted); margin-top:0}
+  .grid{display:grid; grid-template-columns: 1fr 1fr; gap:14px}
+  @media (max-width: 860px){ .grid{grid-template-columns:1fr} }
+  .card{
+    background:var(--card);
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+    border-radius:12px;
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+  .card h3{margin:0 0 10px; font-size:14px; color:var(--muted); font-weight:700}
+  img{max-width:100%; border-radius:12px; border:1px solid var(--bd); background:#000}
+  table{border-collapse:collapse; width:100%; overflow:hidden; border-radius:12px; border:1px solid var(--bd)}
+  th,td{border-bottom:1px solid var(--bd); padding:10px; text-align:left; font-size:13px}
+  th{background:#0b1320; color:var(--muted); font-weight:700}
+  tr:last-child td{border-bottom:none}
+  .ok{color:var(--ok); font-weight:800}
+  .note{color:var(--muted); font-size:13px}
+  .kpiRow{display:flex; flex-wrap:wrap; gap:10px; margin-top:10px}
+  .kpi{
+    background:#0b1320;
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+    padding:10px 12px;
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+  }
+  .kpi .v{font-weight:900; font-size:16px}
+  .kpi .l{color:var(--muted); font-size:12px}
+  footer{margin-top:26px; color:var(--muted); font-size:12px}
 </style>
 </head>
 <body>
+  <div class="hfTopbar">
+    <div class="left">
+      <span class="brand">🗄️ GRPO SQL Optimizer</span>
+      <a class="pill" href="https://huggingface.co/spaces/laterabhi/grpo-sql-optimizer/blob/main/README.md" target="_blank" rel="noopener">📝 Writeup</a>
+      <a class="pill" href="https://huggingface.co/spaces/laterabhi/grpo-sql-optimizer/blob/main/Blog.md" target="_blank" rel="noopener">📄 Blog.md</a>
+      <a class="pill" href="https://huggingface.co/laterabhi/grpo-sql-optimizer" target="_blank" rel="noopener">🤗 Model</a>
+      <a class="pill" href="https://github.com/OfficialAbhinavSingh/SQL-Query-Optimization-Environment-" target="_blank" rel="noopener">💻 GitHub</a>
+    </div>
+    <div class="muted">DuckDB-verifiable rewards · OpenEnv</div>
+  </div>
+  <div class="wrap">
+    <h1>GRPO Training for SQL Query Optimization</h1>
+    <p class="sub">
+      Fine-tuned <code>Qwen/Qwen2.5-0.5B-Instruct</code> using GRPO (Group Relative Policy Optimization)
+      to optimize SQL queries using a DuckDB execution environment.
+    </p>
+    <div class="grid">
+      <div class="card">
+        <h3>Overview</h3>
+        <p>
+          This project trains/evaluates SQL optimization with <strong>execution-grounded</strong> rewards:
+          the environment executes both original and rewritten SQL on real DuckDB data, and scores
+          speedup + correctness + structured diagnostics.
+        </p>
+        <div class="kpiRow">
+          <div class="kpi">
+            <div class="v">5</div>
+            <div class="l">tasks (easy → expert)</div>
+          </div>
+          <div class="kpi">
+            <div class="v">DuckDB</div>
+            <div class="l">verifiable execution</div>
+          </div>
+          <div class="kpi">
+            <div class="v">GRPO</div>
+            <div class="l">group-relative RL</div>
+          </div>
+        </div>
+      </div>
+      <div class="card">
+        <h3>Training curve</h3>
+        <!-- Prefer local image if you uploaded it to this Space -->
+        <img src="grpo_results.png" alt="GRPO training curve" />
+        <p class="note" style="margin-top:10px">
+          If this image ever breaks, the canonical plot is also in the GitHub repo:
+          <a href="https://raw.githubusercontent.com/OfficialAbhinavSingh/SQL-Query-Optimization-Environment-/main/results/grpo_reward_curve.png" target="_blank" rel="noopener">
+            results/grpo_reward_curve.png
+          </a>.
+        </p>
+      </div>
+    </div>
+    <h2>Training progress (100 episodes)</h2>
+    <table>
+      <tr><th>Metric</th><th>Value</th></tr>
+      <tr><td>Start avg (ep 1–10)</td><td>0.3090</td></tr>
+      <tr><td>End avg (ep 91–100)</td><td>0.5962</td></tr>
+      <tr><td><strong>Improvement</strong></td><td><strong>+93%</strong></td></tr>
+    </table>
+    <h2>Final evaluation (per task)</h2>
+    <p class="note">
+      These task scores are aligned to the GitHub repo README (source of truth). Task 5 is the expert scenario,
+      so it is expected to be the lowest — that is not an error.
+    </p>
+    <table>
+      <tr><th>Task</th><th>Difficulty</th><th>Score</th></tr>
+      <tr><td>task_1_basic_antipatterns</td><td>easy</td><td><span class="ok">0.7500 ✅</span></td></tr>
+      <tr><td>task_2_correlated_subqueries</td><td>medium</td><td><span class="ok">0.8313 ✅</span></td></tr>
+      <tr><td>task_3_wildcard_scan</td><td>medium-hard</td><td><span class="ok">0.6563 ✅</span></td></tr>
+      <tr><td>task_4_implicit_join</td><td>hard</td><td><span class="ok">0.6563 ✅</span></td></tr>
+      <tr><td>task_5_window_functions</td><td>expert</td><td><span class="ok">0.6500 ✅</span></td></tr>
+    </table>
+    <h2>Before / After (environment-only, reproducible)</h2>
+    <p class="note">
+      To avoid hand-wavy baselines, we provide a reproducible before/after contrast in the GitHub repo:
+      “before” = analysis-only (no optimized SQL), “after” = deterministic fallback with a real optimized query.
+      Chart:
+      <a href="https://raw.githubusercontent.com/OfficialAbhinavSingh/SQL-Query-Optimization-Environment-/main/results/before_after_chart.png" target="_blank" rel="noopener">
+        results/before_after_chart.png
+      </a>
+    </p>
+    <img
+      src="https://raw.githubusercontent.com/OfficialAbhinavSingh/SQL-Query-Optimization-Environment-/main/results/before_after_chart.png"
+      alt="Before/after chart"
+    />
+    <h2>Approach</h2>
+    <div class="card">
+      <h3>GRPO setup</h3>
+      <ul class="note">
+        <li><strong>Algorithm:</strong> GRPO (Group Relative Policy Optimization)</li>
+        <li><strong>Base model:</strong> Qwen/Qwen2.5-0.5B-Instruct</li>
+        <li><strong>Group size:</strong> 4 completions per prompt</li>
+        <li><strong>Hardware:</strong> Kaggle GPU T4 x2 (see repo links)</li>
+      </ul>
+      <h3 style="margin-top:12px">Reward components</h3>
+      <ul class="note">
+        <li><code>execution_speedup</code>: measured DuckDB timing ratio</li>
+        <li><code>result_correctness</code>: results match check (order-independent for large sets)</li>
+        <li><code>issue_detection</code>: anti-pattern detection vs ground truth keywords</li>
+        <li><code>approval_correctness</code>, <code>summary_quality</code>, <code>severity_labels</code></li>
+      </ul>
+    </div>
+    <h2>Links</h2>
+    <ul class="note">
+      <li><a href="https://huggingface.co/laterabhi/grpo-sql-optimizer" target="_blank" rel="noopener">Model on Hugging Face</a></li>
+      <li><a href="https://github.com/OfficialAbhinavSingh/SQL-Query-Optimization-Environment-" target="_blank" rel="noopener">GitHub repo (source of truth)</a></li>
+      <li><a href="https://arxiv.org/abs/2402.03300" target="_blank" rel="noopener">GRPO paper</a></li>
+    </ul>
+    <footer>
+      Tip: If you want all images to load instantly (no GitHub raw), upload the PNGs into this Space repo and change the <code>src</code> to local file paths.
+    </footer>
+  </div>
 </body>
 </html>