docs(leaderboard): disclose September 2026 evaluation dates

README.md

@@ -33,7 +33,7 @@ Compare RPent with reference methods on LIBERO, LIBERO-PRO, RoboCasa365 Target50
 
 Codex / GPT-6 Astra / low / reasoning: **92.63% Overall (741/800)** across all eight LIBERO-PRO suites. See the [suite results and memory-batch explanation](https://rpent.readthedocs.io/en/latest/rst_source/leaderboard/index.html#libero-pro-astra-memory), including the separately frozen Long and Spatial/Object/Goal memory batches.
 
-[![RPent Leaderboard](https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/leaderboard-en-light.png)](https://rpent.readthedocs.io/en/latest/rst_source/leaderboard/index.html)
+[![RPent Leaderboard](https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/leaderboard-en-light.png)](https://rpent.readthedocs.io/en/latest/rst_source/leaderboard/index.html)
 
 
 ## Who Should Consider Using RPent?

README.zh-CN.md

@@ -33,7 +33,7 @@
 
 Codex / GPT-6 Astra / low / reasoning 已完成全部八套 LIBERO-PRO，**Overall 92.63%（741/800）**。详见 [套件汇总与 memory 批次说明](https://rpent.readthedocs.io/zh-cn/latest/rst_source/leaderboard/index.html#libero-pro-astra-memory)，其中 Long 与 Spatial/Object/Goal 分别使用各自冻结的 memory 批次。
 
-[![RPent 排行榜](https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/leaderboard-zh-light.png)](https://rpent.readthedocs.io/zh-cn/latest/rst_source/leaderboard/index.html)
+[![RPent 排行榜](https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/leaderboard-zh-light.png)](https://rpent.readthedocs.io/zh-cn/latest/rst_source/leaderboard/index.html)
 
 
 ## 适用用户

docs/source-en/rst_source/get_started/overview.rst

@@ -32,13 +32,13 @@ Compare success rates on LIBERO, LIBERO-PRO, RoboCasa365 Target50, and RoboTwin
 C2R. Rankings apply to the methods and evaluation coverage shown; see
 :doc:`../leaderboard/index` for detailed results, configurations, and sources.
 
-.. image:: https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/leaderboard-en-light.png
+.. image:: https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/leaderboard-en-light.png
    :alt: RPent Leaderboard
    :class: only-light
    :width: 100%
    :target: ../leaderboard/index.html
 
-.. image:: https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/leaderboard-en-dark.png
+.. image:: https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/leaderboard-en-dark.png
    :alt: RPent Leaderboard
    :class: only-dark
    :width: 100%

docs/source-en/rst_source/leaderboard/index.rst

@@ -11,7 +11,7 @@ RPent Leaderboard
 
 .. raw:: html
 
-   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/embed.js"></script>
+   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/embed.js"></script>
    <div id="rpent-interactive-leaderboard" data-section="index"
         data-performance-url="performance.html"
         data-costs-url="time-token-costs.html"></div>

docs/source-en/rst_source/leaderboard/performance.rst

@@ -6,20 +6,21 @@ Performance
 .. raw:: html
 
    <p class="leaderboard-page-brand">RPent Leaderboard</p>
-   <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/docs.css">
-   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/table-sort.js"></script>
-   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/leaderboard.js"></script>
-   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/embed.js"></script>
+   <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/docs.css">
+   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/table-sort.js"></script>
+   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/leaderboard.js"></script>
+   <script defer src="https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/embed.js"></script>
    <div id="rpent-interactive-leaderboard" data-language="en" data-section="performance"
         data-performance-url="performance.html" data-costs-url="time-token-costs.html"
-        data-results-url="https://cdn.jsdelivr.net/gh/RLinf/misc@705bd44bfc8ad7586b76239de13167db35abcce7/rpent/benchmarks/results.json">
+        data-results-url="https://cdn.jsdelivr.net/gh/RLinf/misc@756ab5ee0667f658332a34a72910df5e6448dfea/rpent/benchmarks/results.json">
      <div class="rpent-static-leaderboard">
        <section id="native-performance" data-performance="true"><h2 class="page-section-title">Performance</h2><div class="section-layout"><details class="module-directory" open><summary>Environments</summary><nav class="module-nav" aria-label="Performance"><a href="#native-libero-pro">LIBERO-PRO</a><a href="#native-standard-libero">LIBERO</a><a href="#native-robocasa">RoboCasa365</a><a href="#native-robotwin">RoboTwin</a></nav></details><div class="section-results">
        <section data-benchmark="libero-pro"><details data-module="libero-pro"><summary class="module-header"><h3>LIBERO-PRO</h3></summary><div id="native-libero-pro" class="module-content"><table><thead><tr><th>Method</th><th>Success rate</th></tr></thead><tbody><tr><td>Codex / GPT-6 Astra / low / reasoning<sup class="note-reference"><a href="#libero-pro-astra-memory" role="doc-noteref" aria-label="Evaluation note 4 for GPT-6 Astra">[4]</a></sup></td><td>92.63%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>82.4%</td></tr><tr><td>Codex / GPT-5.5 / xhigh / reasoning</td><td>75.13%</td></tr><tr><td>RPent Flash Mode<sup class="note-reference"><a href="#libero-pro-note-2" role="doc-noteref" aria-label="Evaluation note 2 for RPent Flash Mode">[2]</a></sup></td><td>72.63%</td></tr><tr><td>Qwen3.6 27B / no-reasoning</td><td>70.63%</td></tr><tr><td>ASPIRE<sup class="note-reference"><a href="#libero-pro-note-1" role="doc-noteref" aria-label="Evaluation note 1 for ASPIRE">[1]</a></sup></td><td>61.36%</td></tr><tr><td>π_RLinf</td><td>50.0%</td></tr><tr><td>π0.5</td><td>11.0%</td></tr><tr><td>AtomVLA</td><td>6.3%</td></tr><tr><td>X-VLA</td><td>3.8%</td></tr><tr><td>MolmoAct</td><td>1.5%</td></tr><tr><td>π0</td><td>0.3%</td></tr></tbody></table><p>* GPT-6 Astra: 92.63% (741/800). RPent Flash Mode / Molmo2-8B: 72.63% (581/800). Qwen3.6 27B / no-reasoning: 70.63% (565/800).</p><h4>All methods &amp; reported scores</h4><div class="table-wrap" tabindex="0"><table><thead><tr><th>Method</th><th>Overall</th><th>Spatial Task</th><th>Spatial Swap</th><th>Object Task</th><th>Object Swap</th><th>Goal Task</th><th>Goal Swap</th><th>Long Task</th><th>Long Swap</th></tr></thead><tbody><tr><td>GPT-6 Astra<sup class="note-reference"><a href="#libero-pro-astra-memory" role="doc-noteref" aria-label="Evaluation note 4 for GPT-6 Astra">[4]</a></sup></td><td>92.63%</td><td>100%</td><td>98%</td><td>100%</td><td>99%</td><td>88%</td><td>99%</td><td>85%</td><td>72%</td></tr><tr><td>Opus-4.7 / max.reasoning</td><td>82.4%</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>GPT-5.5</td><td>75.13%</td><td>81.0%</td><td>69.0%</td><td>94.0%</td><td>91.0%</td><td>75.0%</td><td>66.0%</td><td>70.00%</td><td>55.00%</td></tr><tr><td>RPent Flash Mode / Molmo2-8B<sup class="note-reference"><a href="#libero-pro-note-2" role="doc-noteref" aria-label="Evaluation note 2 for RPent Flash Mode">[2]</a></sup></td><td>72.63%</td><td>79.00%</td><td>70.00%</td><td>86.00%</td><td>93.00%</td><td>74.00%</td><td>65.00%</td><td>60.00%</td><td>54.00%</td></tr><tr><td>Qwen3.6 27B / no-reasoning</td><td>70.63%</td><td>82.00%</td><td>78.00%</td><td>83.00%</td><td>84.00%</td><td>68.00%</td><td>68.00%</td><td>61.00%</td><td>41.00%</td></tr><tr><td>ASPIRE<sup class="note-reference"><a href="#libero-pro-note-1" role="doc-noteref" aria-label="Evaluation note 1 for ASPIRE">[1]</a></sup></td><td>61.36%</td><td>60.0%</td><td>51.0%</td><td>95.0%</td><td>98.0%</td><td>45.0%</td><td>81.0%</td><td>38.3%</td><td>22.6%</td></tr><tr><td>π_RLinf</td><td>50.0%</td><td>42.0%</td><td>59.0%</td><td>71.0%</td><td>78.0%</td><td>45.0%</td><td>42.0%</td><td>49.0%</td><td>14.0%</td></tr><tr><td>π0.5</td><td>11.0%</td><td>1.0%</td><td>20.0%</td><td>1.0%</td><td>17.0%</td><td>2.0%</td><td>38.0%</td><td>1.0%</td><td>8.0%</td></tr><tr><td>AtomVLA</td><td>6.3%</td><td>1.0%</td><td>16.0%</td><td>0.0%</td><td>10.0%</td><td>11.0%</td><td>2.0%</td><td>9.0%</td><td>1.0%</td></tr><tr><td>X-VLA</td><td>3.8%</td><td>0.0%</td><td>0.0%</td><td>8.0%</td><td>2.0%</td><td>9.0%</td><td>1.0%</td><td>10.0%</td><td>0.0%</td></tr><tr><td>MolmoAct</td><td>1.5%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>6.0%</td><td>0.0%</td><td>0.0%</td><td>6.0%</td><td>0.0%</td></tr><tr><td>π0</td><td>0.3%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>2.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td></tr><tr><td>Cap-X</td><td>—</td><td>14.0%</td><td>12.0%</td><td>18.0%</td><td>22.0%</td><td>17.0%</td><td>26.0%</td><td>—</td><td>—</td></tr><tr><td>RPent / GPT-6 Motor Only<sup class="note-reference"><a href="#libero-pro-note-3" role="doc-noteref" aria-label="Evaluation note 3 for RPent / GPT-6 Motor Only">[3]</a></sup></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>38.0%</td><td>—</td></tr><tr><td>RATS</td><td>—</td><td>31.0%</td><td>29.0%</td><td>63.0%</td><td>61.0%</td><td>36.0%</td><td>43.0%</td><td>—</td><td>—</td></tr></tbody></table></div><p id="libero-pro-note-1" class="scope-note" tabindex="-1" role="note">* <span class="note-number">[1]</span> ASPIRE: Long Task and Long Swap use zero-shot transfer from the LIBERO-90 skill library.</p><p id="libero-pro-note-2" class="scope-note" tabindex="-1" role="note">* <span class="note-number">[2]</span> RPent Flash Mode uses directly downloaded, officially released GPT-5.5 exploration memory; Molmo2-8B is used for visual localization. These results use the best-performing seed from s0-s9.</p><p id="libero-pro-note-3" class="scope-note" tabindex="-1" role="note">* <span class="note-number">[3]</span> RPent / GPT-6 Motor Only uses motor-only control: it directly outputs end-effector pose increments and gripper commands through execute_action, without invoking VLA / primitives or loading memory.</p><p id="libero-pro-astra-memory" class="scope-note memory-context" tabindex="-1" role="note">* <span class="note-number">[4]</span> GPT-6 Astra (memory-enabled configuration): Long Task/Swap and the other six suites use separate memory-file snapshots frozen after their respective exploration phases, with no updates during evaluation. Overall combines two non-overlapping batches: Long 157/200 plus the other suites 584/600, giving 741/800 (92.63%); the 800 episodes do not share a single memory snapshot.</p></div></details></section>
        <section data-benchmark="standard-libero"><details data-module="standard-libero"><summary class="module-header"><h3>LIBERO</h3></summary><div id="native-standard-libero" class="module-content"><table><thead><tr><th>Method</th><th>Success rate</th></tr></thead><tbody><tr><td>AtomVLA</td><td>97.0%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>96.0%</td></tr><tr><td>π_RLinf</td><td>95.3%</td></tr><tr><td>π0</td><td>94.2%</td></tr><tr><td>NORA</td><td>79.5%</td></tr><tr><td>OpenVLA</td><td>76.5%</td></tr></tbody></table></div></details></section>
        <section data-benchmark="robocasa"><details data-module="robocasa"><summary class="module-header"><h3>RoboCasa365 · Target50</h3></summary><div id="native-robocasa" class="module-content"><table><thead><tr><th>Method</th><th>Success rate</th></tr></thead><tbody><tr><td>Codex / GPT-6 Astra / low / reasoning</td><td>59.20%</td></tr><tr><td>Xiaomi-Robotics-1</td><td>57.4%</td></tr><tr><td>Codex / GPT-5.5 / xhigh / reasoning</td><td>57.1%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>48.6%</td></tr><tr><td>WorldDreamer</td><td>35.3%</td></tr><tr><td>RLDX-1</td><td>30.0%</td></tr><tr><td>π0.5</td><td>16.9%</td></tr><tr><td>π0</td><td>14.8%</td></tr></tbody></table><h4>RoboCasa365 · Target50 · All methods &amp; reported scores</h4><div class="table-wrap" tabindex="0"><table><thead><tr><th>Method</th><th>Overall</th><th>Atomic-Seen</th><th>Composite-Seen</th><th>Composite-Unseen</th></tr></thead><tbody><tr><td>GPT-6 Astra</td><td>59.20%</td><td>87.78%</td><td>43.75%</td><td>42.50%</td></tr><tr><td>Xiaomi-Robotics-1</td><td>57.4%</td><td>80.2%</td><td>57.1%</td><td>32.1%</td></tr><tr><td>GPT-5.5</td><td>57.1%</td><td>92.0%</td><td>61.0%</td><td>13.8%</td></tr><tr><td>Opus-4.7 / max.reasoning</td><td>48.6%</td><td>79.4%</td><td>47.5%</td><td>15.0%</td></tr><tr><td>WorldDreamer</td><td>35.3%</td><td>66.3%</td><td>26.7%</td><td>9.0%</td></tr><tr><td>RLDX-1</td><td>30.0%</td><td>60.0%</td><td>21.3%</td><td>5.0%</td></tr><tr><td>π0.5</td><td>16.9%</td><td>39.6%</td><td>7.1%</td><td>1.2%</td></tr><tr><td>π0</td><td>14.8%</td><td>34.6%</td><td>6.1%</td><td>1.1%</td></tr></tbody></table></div></div></details></section>
        <section data-benchmark="robotwin"><details data-module="robotwin"><summary class="module-header"><h3>RoboTwin</h3></summary><div id="native-robotwin" class="module-content"><table><thead><tr><th>Method</th><th>Success rate</th></tr></thead><tbody><tr><td>Codex / GPT-5.5 / xhigh / reasoning</td><td>62.4%</td></tr><tr><td>Claude Code / Opus-4.7 / max.reasoning</td><td>58.4%</td></tr><tr><td>LingBot-VLA</td><td>50.4%</td></tr><tr><td>π0.5</td><td>47.9%</td></tr><tr><td>GR00T-N1.7</td><td>20.7%</td></tr><tr><td>StarVLA</td><td>10.6%</td></tr></tbody></table></div></details></section>
        </div></div></section>
+       <div class="leaderboard-footer"><p class="scope-note evaluation-notice">* All RPent method results shown in this leaderboard are from September 2026 evaluations.</p><span>RPent Leaderboard</span></div>
 
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