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VPNavBarMenuGroup" data-v-e0cd9371 data-v-ffaaba87><button type="button" class="button" aria-haspopup="true" aria-expanded="false" data-v-ffaaba87><span class="text" data-v-ffaaba87><!----><span data-v-ffaaba87>📁 旧文</span><span class="vpi-chevron-down text-icon" data-v-ffaaba87></span></span></button><div class="menu" data-v-ffaaba87><div class="VPMenu" data-v-ffaaba87 data-v-798b97ca><div class="items" data-v-798b97ca><!--[--><!--[--><div class="VPMenuGroup" data-v-798b97ca data-v-a2651156><p class="title" data-v-a2651156>🗣️ NLP</p><!--[--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/nlp.html" data-v-dc987abe><!--[--><span data-v-dc987abe>📝 自然语言处理</span><!--]--></a></div><!--]--><!--]--></div><!--]--><!--[--><div class="VPMenuGroup" data-v-798b97ca data-v-a2651156><p class="title" data-v-a2651156>🧬 基础理论</p><!--[--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/dl.html" data-v-dc987abe><!--[--><span data-v-dc987abe>🕸️ 深度学习</span><!--]--></a></div><!--]--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/rl.html" data-v-dc987abe><!--[--><span data-v-dc987abe>🔁 强化学习</span><!--]--></a></div><!--]--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/ml.html" data-v-dc987abe><!--[--><span data-v-dc987abe>📊 机器学习</span><!--]--></a></div><!--]--><!--]--></div><!--]--><!--[--><div class="VPMenuGroup" data-v-798b97ca data-v-a2651156><p class="title" data-v-a2651156>🧮 算法专栏</p><!--[--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/algo/" data-v-dc987abe><!--[--><span data-v-dc987abe>🔢 算法题</span><!--]--></a></div><!--]--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/bigdata/" data-v-dc987abe><!--[--><span data-v-dc987abe>☁️ 大数据</span><!--]--></a></div><!--]--><!--]--></div><!--]--><!--[--><div class="VPMenuGroup" data-v-798b97ca data-v-a2651156><p class="title" data-v-a2651156>☕ 其他</p><!--[--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/env/" data-v-dc987abe><!--[--><span data-v-dc987abe>🛠️ 环境搭建</span><!--]--></a></div><!--]--><!--[--><div class="VPMenuLink" data-v-a2651156 data-v-dc987abe><a class="VPLink link" href="/posts/olds/other/" data-v-dc987abe><!--[--><span data-v-dc987abe>🍃 其他</span><!--]--></a></div><!--]--><!--]--></div><!--]--><!--]--></div><!--[--><!--]--></div></div></div><!--]--><!--[--><a class="VPLink link VPNavBarMenuLink" href="/posts/archive.html" tabindex="0" data-v-e0cd9371 data-v-4aa19863><!--[--><span data-v-4aa19863>📅 归档</span><!--]--></a><!--]--><!--[--><a class="VPLink link VPNavBarMenuLink" href="/posts/me.html" tabindex="0" data-v-e0cd9371 data-v-4aa19863><!--[--><span data-v-4aa19863>🙋 关于我</span><!--]--></a><!--]--><!--]--></nav><!----><div class="VPNavBarAppearance appearance" data-v-ce71e4ef data-v-b59ebfac><button class="VPSwitch VPSwitchAppearance" type="button" role="switch" title aria-checked="false" data-v-b59ebfac data-v-809b7594 data-v-3591f5e2><span class="check" data-v-3591f5e2><span class="icon" data-v-3591f5e2><!--[--><span class="vpi-sun sun" data-v-809b7594></span><span class="vpi-moon moon" data-v-809b7594></span><!--]--></span></span></button></div><div class="VPSocialLinks VPNavBarSocialLinks social-links" data-v-ce71e4ef data-v-e0f2db57 data-v-7a4bfff1><!--[--><a class="VPSocialLink no-icon" href="https://github.com/plmsmile" aria-label="github" target="_blank" rel="noopener" data-v-7a4bfff1 data-v-8e5dce54><span class="vpi-social-github"></span></a><!--]--></div><div class="VPFlyout VPNavBarExtra extra" data-v-ce71e4ef data-v-8897e953 data-v-ffaaba87><button type="button" class="button" aria-haspopup="true" aria-expanded="false" 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href="https://github.com/plmsmile" aria-label="github" target="_blank" rel="noopener" data-v-7a4bfff1 data-v-8e5dce54><span class="vpi-social-github"></span></a><!--]--></div></div></div><!--]--><!--]--></div></div></div><!--[--><!--]--><button type="button" class="VPNavBarHamburger hamburger" aria-label="mobile navigation" aria-expanded="false" aria-controls="VPNavScreen" data-v-ce71e4ef data-v-37e0f734><span class="container" data-v-37e0f734><span class="top" data-v-37e0f734></span><span class="middle" data-v-37e0f734></span><span class="bottom" data-v-37e0f734></span></span></button></div></div></div></div><div class="divider" data-v-ce71e4ef><div class="divider-line" data-v-ce71e4ef></div></div></div><!----></header><div class="VPLocalNav empty fixed" data-v-cecb633e data-v-1b409c8b><div class="container" data-v-1b409c8b><!----><div class="VPLocalNavOutlineDropdown" style="--vp-vh:0px;" data-v-1b409c8b data-v-a203161a><button data-v-a203161a>Return to top</button><!----></div></div></div><!----><div class="VPContent" id="VPContent" data-v-cecb633e data-v-53a9cb18><div class="VPDoc has-aside" data-v-53a9cb18 data-v-5a64a79a><!--[--><!--]--><div class="container" data-v-5a64a79a><div class="aside" data-v-5a64a79a><div class="aside-curtain" data-v-5a64a79a></div><div class="aside-container" data-v-5a64a79a><div class="aside-content" data-v-5a64a79a><div class="VPDocAside" data-v-5a64a79a data-v-f8ea3c28><!--[--><!--]--><!--[--><!--]--><nav aria-labelledby="doc-outline-aria-label" class="VPDocAsideOutline" data-v-f8ea3c28 data-v-b94d89ac><div class="content" data-v-b94d89ac><div class="outline-marker" data-v-b94d89ac></div><div aria-level="2" class="outline-title" id="doc-outline-aria-label" role="heading" data-v-b94d89ac>当前页大纲</div><ul class="VPDocOutlineItem root" data-v-b94d89ac data-v-80b46526><!--[--><!--]--></ul></div></nav><!--[--><!--]--><div class="spacer" data-v-f8ea3c28></div><!--[--><!--]--><!----><!--[--><!--]--><!--[--><!--]--></div></div></div></div><div class="content" data-v-5a64a79a><div class="content-container" data-v-5a64a79a><!--[--><!--[--><!--[--><article class="post-header" data-v-a99fd7c9><h1 class="title" data-v-a99fd7c9></h1><div class="stats-container" data-v-a99fd7c9><!----><div class="stat-item" data-v-a99fd7c9> 📅 发表于 <span class="stat-text" data-v-a99fd7c9>2025/07/23</span></div><div class="stat-item" data-v-a99fd7c9> 🔄 更新于 <span class="stat-text" data-v-a99fd7c9>Invalid Date</span></div><div class="stat-divider" data-v-a99fd7c9></div><div class="stat-item" data-v-a99fd7c9> 👁️ <span class="stat-text" data-v-a99fd7c9>-- 次访问</span><span id="busuanzi_value_page_pv" style="display:none;" data-page="/api-examples.html" data-v-a99fd7c9></span></div><div class="stat-divider" data-v-a99fd7c9></div><div class="stat-item" data-v-a99fd7c9> 📝 <span class="stat-text" data-v-a99fd7c9>0 字</span></div><div class="stat-divider" data-v-a99fd7c9></div><div class="stat-item" data-v-a99fd7c9> ⏳ <span class="stat-text" data-v-a99fd7c9>0 分钟</span></div><div class="stat-divider" data-v-a99fd7c9></div></div><!----></article><!--]--><!--]--><!--]--><main class="main" data-v-5a64a79a><div style="position:relative;" class="vp-doc _api-examples" data-v-5a64a79a><div><h1 id="runtime-api-examples" tabindex="-1">Runtime API Examples <a class="header-anchor" href="#runtime-api-examples" aria-label="Permalink to "Runtime API Examples""></a></h1><p>This page demonstrates usage of some of the runtime APIs provided by VitePress.</p><p>The main <code>useData()</code> API can be used to access site, theme, and page data for the current page. It works in both <code>.md</code> and <code>.vue</code> files:</p><div class="language-md vp-adaptive-theme line-numbers-mode"><button title="Copy Code" class="copy"></button><span class="lang">md</span><pre class="shiki shiki-themes one-light one-dark-pro vp-code" tabindex="0"><code><span class="line"><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"><</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">script</span><span style="--shiki-light:#986801;--shiki-dark:#D19A66;"> setup</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">></span></span>
<span class="line"><span style="--shiki-light:#A626A4;--shiki-dark:#C678DD;">import</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"> { </span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">useData</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"> } </span><span style="--shiki-light:#A626A4;--shiki-dark:#C678DD;">from</span><span style="--shiki-light:#50A14F;--shiki-dark:#98C379;"> 'vitepress'</span></span>
<span class="line"></span>
<span class="line"><span style="--shiki-light:#A626A4;--shiki-dark:#C678DD;">const</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"> { </span><span style="--shiki-light:#986801;--shiki-dark:#E5C07B;">theme</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">, </span><span style="--shiki-light:#986801;--shiki-dark:#E5C07B;">page</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">, </span><span style="--shiki-light:#986801;--shiki-dark:#E5C07B;">frontmatter</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"> } </span><span style="--shiki-light:#0184BC;--shiki-dark:#56B6C2;">=</span><span style="--shiki-light:#4078F2;--shiki-dark:#61AFEF;"> useData</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">()</span></span>
<span class="line"><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"></</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">script</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">></span></span>
<span class="line"></span>
<span class="line"><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">## Results</span></span>
<span class="line"></span>
<span class="line"><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">### Theme Data</span></span>
<span class="line"><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"><</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">pre</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">>{{ theme }}</</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">pre</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">></span></span>
<span class="line"></span>
<span class="line"><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">### Page Data</span></span>
<span class="line"><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"><</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">pre</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">>{{ page }}</</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">pre</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">></span></span>
<span class="line"></span>
<span class="line"><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">### Page Frontmatter</span></span>
<span class="line"><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;"><</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">pre</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">>{{ frontmatter }}</</span><span style="--shiki-light:#E45649;--shiki-dark:#E06C75;">pre</span><span style="--shiki-light:#383A42;--shiki-dark:#ABB2BF;">></span></span></code></pre><div class="line-numbers-wrapper" aria-hidden="true"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br></div></div><h2 id="results" tabindex="-1">Results <a class="header-anchor" href="#results" aria-label="Permalink to "Results""></a></h2><h3 id="theme-data" tabindex="-1">Theme Data <a class="header-anchor" href="#theme-data" aria-label="Permalink to "Theme Data""></a></h3><pre>{
"nav": [
{
"text": "🏠 首页",
"link": "/"
},
{
"text": "🐲 LLM",
"items": [
{
"text": "🧱 基础建设",
"items": [
{
"text": "📖 基础知识",
"link": "/posts/llm/basic/08-llm-position-embedding"
},
{
"text": "🛠️ 基建框架",
"link": "/posts/llm/infra/06-verl-code"
}
]
},
{
"text": "🔄 强化学习",
"items": [
{
"text": "🎓 RL理论基础",
"link": "/posts/llm/rl/theory/01-rl-introduction"
},
{
"text": "🕹️ AgentRL",
"link": "/posts/llm/rl/agentrl/01-category"
}
]
},
{
"text": "🌟 行业方向",
"items": [
{
"text": "🚀 基础模型",
"link": "/posts/llm/industry/mainllm/01-kimi-series"
},
{
"text": "💻 Code模型",
"link": "/posts/llm/industry/codellm/07-code-fulltrain-reading"
}
]
}
]
},
{
"text": "🌐 领域",
"items": [
{
"text": "🏹 领域任务",
"items": [
{
"text": "👨💻 SWE 系列",
"link": "/posts/domain/swe/01-swe-basic"
},
{
"text": "📱 AppWorld 系列",
"link": "/posts/domain/appworld/01-appworld-rl"
},
{
"text": "🦞 Claw 系列",
"link": "/posts/domain/claw/01-claw-bench"
}
]
},
{
"text": "🤖 Agent",
"items": [
{
"text": "💡 概念杂项",
"link": "/posts/agent/basic/01-lhy-agent-notes"
},
{
"text": "🦞 OpenClaw",
"link": "/posts/agent/openclaw/01-nanoagent"
}
]
},
{
"text": "📦 其他",
"items": [
{
"text": "🌐 搜索 (少量论文)",
"link": "/posts/domain/search/01-agent-search"
},
{
"text": "🛠️ ToolUse (少量论文)",
"link": "/posts/domain/tooluse/01-agent-tool"
}
]
}
]
},
{
"text": "✍️ 经验",
"items": [
{
"text": "🧩 刷题",
"items": [
{
"text": "🧮 LeetCode",
"link": "/posts/exps/leetcode/01-hash"
}
]
},
{
"text": "⚙️ 配置",
"items": [
{
"text": "🔧 环境搭建",
"link": "/posts/exps/env/01-blog-env"
}
]
},
{
"text": "🧘 心得",
"items": [
{
"text": "💭 心得体会",
"link": "/posts/exps/mind"
}
]
}
]
},
{
"text": "📁 旧文",
"items": [
{
"text": "🗣️ NLP",
"items": [
{
"text": "📝 自然语言处理",
"link": "/posts/olds/nlp"
}
]
},
{
"text": "🧬 基础理论",
"items": [
{
"text": "🕸️ 深度学习",
"link": "/posts/olds/dl"
},
{
"text": "🔁 强化学习",
"link": "/posts/olds/rl"
},
{
"text": "📊 机器学习",
"link": "/posts/olds/ml"
}
]
},
{
"text": "🧮 算法专栏",
"items": [
{
"text": "🔢 算法题",
"link": "/posts/olds/algo/"
},
{
"text": "☁️ 大数据",
"link": "/posts/olds/bigdata/"
}
]
},
{
"text": "☕ 其他",
"items": [
{
"text": "🛠️ 环境搭建",
"link": "/posts/olds/env/"
},
{
"text": "🍃 其他",
"link": "/posts/olds/other/"
}
]
}
]
},
{
"text": "📅 归档",
"link": "/posts/archive.md"
},
{
"text": "🙋 关于我",
"link": "/posts/me"
}
],
"outline": {
"level": [
1,
4
],
"label": "当前页大纲"
},
"socialLinks": [
{
"icon": "github",
"link": "https://github.com/plmsmile"
}
],
"search": {
"provider": "local"
},
"docFooter": {
"prev": "上一页",
"next": "下一页"
},
"sidebar": {
"/posts/olds/nlp/": {
"base": "/posts/olds/nlp/",
"items": [
{
"text": "💬 NLP",
"items": [
{
"text": "机器阅读(二)--模型(未完成)",
"link": "54-mrc-models"
},
{
"text": "机器阅读(一)--整体概述",
"link": "53-mrc-brief"
},
{
"text": "BERT 详解",
"link": "52-bert"
},
{
"text": "OpenAI GPT：Improving Language Understanding by Generative Pre-Training",
"link": "51-opengpt"
},
{
"text": "ELMo：Deep Contextualized Word Representations",
"link": "50-elmo"
},
{
"text": "QANet",
"link": "49-qanet"
},
{
"text": "Transformer",
"link": "48-attention-is-all-you-need"
},
{
"text": "Bidirectional Attention Flow",
"link": "47-bidaf"
},
{
"text": "R-Net (Gated Self-Matching Networks)",
"link": "46-rnet-selfmatch"
},
{
"text": "Match-LSTM and Answer Pointer",
"link": "45-match-lstm"
},
{
"text": "阅读理解模型总结",
"link": "39-squard-models"
},
{
"text": "阿里小蜜论文",
"link": "36-alime-chat"
},
{
"text": "各种注意力总结",
"link": "33-attention-summary"
},
{
"text": "Dynamic Coattention Network (Plus)",
"link": "32-dynamic-coattention-network"
},
{
"text": "协同注意力简介",
"link": "31-co-attention-vqa"
},
{
"text": "使用Dynamic Memory Network实现一个简单QA",
"link": "30-dynamic-memory-network"
},
{
"text": "词性标注和句法依存的表示符号",
"link": "11-nlp-labels"
},
{
"text": "Word2vec之总体介绍",
"link": "cs224n-notes1-word2vec"
},
{
"text": "Word2vec之数学模型",
"link": "word2vec-math"
},
{
"text": "Word2vec之公式推导笔记",
"link": "cs224n-lecture2-word2vec"
},
{
"text": "subword-units",
"link": "subword-units"
},
{
"text": "Wordpiece模型",
"link": "26-wordpieacemodel"
},
{
"text": "谷歌RNN翻译模型",
"link": "25-google-nmt"
},
{
"text": "机器翻译注意力机制及其PyTorch实现",
"link": "Attention-based-NMT"
},
{
"text": "图文介绍RNN注意力机制",
"link": "attention-model"
},
{
"text": "最初RNN神经翻译简略笔记",
"link": "NMT"
},
{
"text": "语言模型和平滑方法",
"link": "nlp-notes"
},
{
"text": "利用tensorflow实现简版word2vec",
"link": "word2vec"
}
]
}
]
},
"/posts/olds/dl/": {
"base": "/posts/olds/dl/",
"items": [
{
"text": "🧠 DL",
"items": [
{
"text": "卷积神经网络总结",
"link": "38-convolution"
},
{
"text": "网络优化",
"link": "35-nerual-network-optim"
},
{
"text": "cs224n作业一",
"link": "cs224n-assignment-1"
},
{
"text": "cs231n线性分类器和损失函数",
"link": "cs231n-linear-notes"
},
{
"text": "神经网络-过拟合-预处理-BN",
"link": "cs224n-notes3-neural-networks-2"
},
{
"text": "神经网络基础-反向传播-激活函数",
"link": "cs224n-notes3-neural-networks"
},
{
"text": "循环神经网络",
"link": "rnn"
},
{
"text": "PyTorch快速上手",
"link": "23-pytorch-start"
}
]
}
]
},
"/posts/olds/rl/": {
"base": "/posts/olds/rl/",
"items": [
{
"text": "🕹️ RL",
"items": [
{
"text": "强化学习在NLP中的应用",
"link": "44-reinforce-nlp"
},
{
"text": "意图识别和槽填充",
"link": "43-intent-detection-slot-filling"
},
{
"text": "强化学习算法小结",
"link": "42-reinforce-conclusion-simple"
},
{
"text": "基于策略函数的学习方法",
"link": "41-strategy-learning"
},
{
"text": "基于值函数的学习",
"link": "40-value-learning"
},
{
"text": "强化学习",
"link": "37-reinforce-learning"
}
]
}
]
},
"/posts/olds/ml/": {
"base": "/posts/olds/ml/",
"items": [
{
"text": "🤖 ML",
"items": [
{
"text": "决策树笔记",
"link": "29-desicion-tree"
},
{
"text": "机器学习知识点汇总整理",
"link": "28-ml-interview-notes"
},
{
"text": "SVM笔记",
"link": "27-svm-notes"
},
{
"text": "树的总结",
"link": "10-trees"
},
{
"text": "条件随机场",
"link": "crf"
},
{
"text": "最大熵模型",
"link": "maxentmodel"
},
{
"text": "线性回归和逻辑回归",
"link": "21-lr"
},
{
"text": "最大期望算法",
"link": "14-em"
},
{
"text": "马尔可夫模型",
"link": "pgm-01"
},
{
"text": "朴素贝叶斯算法及其代码实现",
"link": "22-ml-ch03-bayes"
}
]
}
]
},
"/posts/olds/algo/": {
"base": "/posts/olds/algo/",
"items": [
{
"text": "🔢 算法题",
"items": [
{
"text": "剑指offer4(51-64)",
"link": "aim2offer4"
},
{
"text": "剑指offer3(21-40)",
"link": "aim2offer3"
},
{
"text": "数据结构之搜索算法",
"link": "algorithm-dfs"
},
{
"text": "leetcode-01",
"link": "leetcode-01"
},
{
"text": "剑指offer(11-20)",
"link": "aim2offer2"
},
{
"text": "排序算法总结",
"link": "sort-algorithms"
},
{
"text": "剑指Offer(1-10)",
"link": "aim2offer"
}
]
}
]
},
"/posts/olds/bigdata/": {
"base": "/posts/olds/bigdata/",
"items": [
{
"text": "📊 BIG Data",
"items": [
{
"text": "NumPy",
"link": "20-numpy"
},
{
"text": "Spark基础编程核心思想介绍",
"link": "19-spark-programming"
},
{
"text": "Spark-SQL的简略笔记",
"link": "18-spark-sql"
},
{
"text": "Spark键值对RDD的常用API",
"link": "17-spark-pairrdd"
},
{
"text": "Spark基础RDD的常用API",
"link": "16-Spark-BaseRDD"
}
]
}
]
},
"/posts/olds/env/": {
"base": "/posts/olds/env/",
"items": [
{
"text": "🔨 环境搭建",
"items": [
{
"text": "IDE配置",
"link": "12-ide-envs"
},
{
"text": "Linux使用笔记",
"link": "09-linux-notes"
},
{
"text": "博客搭建及相关问题",
"link": "24-hexo-problems"
},
{
"text": "旧版博客搭建过程及其问题",
"link": "13-old-blog-problems"
}
]
}
]
},
"/posts/olds/other/": {
"base": "/posts/olds/other/",
"items": [
{
"text": "📦 其他",
"items": [
{
"text": "C++类对象和指针的区别",
"link": "15-cpp-pointer-object-reference"
}
]
}
]
},
"/posts/agent/basic/": {
"base": "/posts/agent/basic/",
"items": [
{
"text": "🐣 Agent-基础(早期)",
"items": [
{
"text": "DeepResearch 评估",
"link": "06-deepresearch-evaluation"
},
{
"text": "Computer-Agent",
"link": "05-comuter-agent"
},
{
"text": "Agent-Interaction-RL 笔记",
"link": "04-agent-env"
},
{
"text": "Agent-RL 综述型笔记",
"link": "01-agent-rl"
},
{
"text": "Agent 思考性文章",
"link": "04-agent-blogs"
},
{
"text": "一些流行的Agents",
"link": "03-current-agents"
},
{
"text": "Agent 评估 Benchmarks",
"link": "02-evaluation-agent"
},
{
"text": "Agent基础概念 (李宏毅笔记)",
"link": "01-lhy-agent-notes"
}
]
}
]
},
"/posts/agent/openclaw/": {
"base": "/posts/agent/openclaw/",
"items": [
{
"text": "🦞 OpenClaw",
"items": [
{
"text": "NanoAgent 核心笔记",
"link": "01-nanoagent"
}
]
}
]
},
"/posts/llm/rl/theory/": {
"base": "/posts/llm/rl/theory/",
"items": [
{
"text": "📜 RL理论基础",
"items": [
{
"text": "On Policy Distillation",
"link": "13-on-policy-distillation"
},
{
"text": "熵和RL相关文章",
"link": "12-entropy"
},
{
"text": "GRPO 改进系列",
"link": "11-grpo-series"
},
{
"text": "PPO 改进系列",
"link": "10-ppo-series"
},
{
"text": "典型策略提升方法：TRPO+PPO+DPO+GRPO",
"link": "09-policy-trpo-ppo"
},
{
"text": "确定性策略梯度",
"link": "08-deterministic-policy-gradient"
},
{
"text": "Actor-Critic 算法",
"link": "07-actor-critic"
},
{
"text": "策略梯度算法",
"link": "06-policy-gradient"
},
{
"text": "DQN算法及进阶",
"link": "05-dqn"
},
{
"text": "免模型预测和控制",
"link": "04-model-free-prediction-control"
},
{
"text": "有模型预测和控制",
"link": "03-model-based-prediction-control"
},
{
"text": "马尔可夫决策过程",
"link": "02-markove-process"
},
{
"text": "强化学习基本概念",
"link": "01-rl-introduction"
},
{
"text": "(18年笔记)强化学习算法小结",
"link": "04-reinforce-conclusion-simple"
},
{
"text": "(18年笔记)基于策略函数的学习方法",
"link": "03-strategy-learning"
},
{
"text": "(18年笔记)基于值函数的学习",
"link": "02-value-learning"
},
{
"text": "(18年笔记)强化学习基础",
"link": "01-reinforce-learning"
}
]
}
]
},
"/posts/llm/rl/agentrl/": {
"base": "/posts/llm/rl/agentrl/",
"items": [
{
"text": "🤖🔁 AgentRL",
"items": [
{
"text": "AgentRL 目录",
"link": "01-category"
},
{
"text": "Agent-RL 相关算法",
"link": "02-agentrl-algo"
}
]
}
]
},
"/posts/llm/industry/mainllm/": {
"base": "/posts/llm/industry/mainllm/",
"items": [
{
"text": "🚀 主流模型",
"items": [
{
"text": "常见模型架构参数汇总",
"link": "16-common-llm-structure"
},
{
"text": "快手系列",
"link": "12-kwai-series"
},
{
"text": "腾讯系列",
"link": "11-tencent-series"
},
{
"text": "Claude 系列",
"link": "09-claude-series"
},
{
"text": "LongCat 系列",
"link": "10-longcat-series"
},
{
"text": "Gemini 系列",
"link": "08-gemini-series"
},
{
"text": "Seed 系列",
"link": "06-seed-series"
},
{
"text": "OpenAI 系列",
"link": "07-openai-series"
},
{
"text": "Qwen 系列",
"link": "05-qwen-series"
},
{
"text": "MiniMax 系列",
"link": "04-minimax-series"
},
{
"text": "GLM 系列",
"link": "03-glm-series"
},
{
"text": "DeepSeek 系列",
"link": "02-deepseek-series"
},
{
"text": "Kimi 系列",
"link": "01-kimi-series"
},
{
"text": "SkyWork 系列",
"link": "15-skywork-series"
},
{
"text": "小米系列",
"link": "14-mimo-series"
},
{
"text": "Nvidia 系列",
"link": "13-nvidia-series"
}
]
}
]
},
"/posts/llm/industry/codellm/": {
"base": "/posts/llm/industry/codellm/",
"items": [
{
"text": "💻 代码模型",
"items": [
{
"text": "SWE 合成数据 系列",
"link": "11-swe-data-series"
},
{
"text": "SWE 总结索引",
"link": "10-swe-summary"
},
{
"text": "Code 全训练 论文阅读",
"link": "07-code-fulltrain-reading"
},
{
"text": "Code TaskRL 论文阅读",
"link": "06-code-taskrl-reading"
},
{
"text": "CodeLLM 索引简记",
"link": "05-open-codellm"
},
{
"text": "Code 安全相关",
"link": "04-safety-code"
},
{
"text": "Code RL 任务",
"link": "03-rl-task"
},
{
"text": "Code 任务Bench相关",
"link": "02-eval-task-benchmark"
},
{
"text": "Code Survey",
"link": "01-survey"
},
{
"text": "SWE 训练方法 系列",
"link": "09-swe-series"
},
{
"text": "Code 预训练相关",
"link": "08-code-pretrain-summary"
}
]
}
]
},
"/posts/llm/basic/": {
"base": "/posts/llm/basic/",
"items": [
{
"text": "🧱 LLM-基础",
"items": [
{
"text": "LLM位置编码和长度外推系列",
"link": "08-llm-position-embedding"
},
{
"text": "LLM 解码相关",
"link": "07-decode"
},
{
"text": "LLM Attention 系列",
"link": "06-llm-attention"
},
{
"text": "LLM Attention 系列",
"link": "01-lm-define-information-theory"
},
{
"text": "LLM 基础知识",
"link": "05-llm-basic-info"
},
{
"text": "LLM 架构相关",
"link": "04-llm-architecture"
},
{
"text": "Transformer细节",
"link": "03-transformer-detail"
},
{
"text": "语言模型重要组件",
"link": "02-llm-components"
}
]
}
]
},
"/posts/llm/infra/": {
"base": "/posts/llm/infra/",
"items": [
{
"text": "🛠 LLM-基建框架",
"items": [
{
"text": "训练观测指标",
"link": "09-train-practice"
},
{
"text": "Verl 训练流程源代码阅读",
"link": "08-verl-train-loop"
},
{
"text": "Verl 有趣的功能",
"link": "07-verl-core"
},
{
"text": "Verl AgentLoop Rollout 相关",
"link": "06-verl-code"
},
{
"text": "Verl 常见参数配置和理解",
"link": "05-verl-practice"
},
{
"text": "Verl 早期概念型学习文章",
"link": "04-verl"
},
{
"text": "推理优化技术",
"link": "03-inference-tech"
},
{
"text": "分布式训练框架",
"link": "02-speed-framework"
},
{
"text": "分布式并行策略",
"link": "01-parrallel"
}
]
}
]
},
"/posts/exps/env/": {
"base": "/posts/exps/env/",
"items": [
{
"text": "⚙️ 环境搭建",
"items": [
{
"text": "一些疑难杂症",
"link": "06-some-problem"
},
{
"text": "Qwen3.5 RL 环境踩坑",
"link": "05-train-qwen35"
},
{
"text": "Agent 安装记录",
"link": "04-claude-code-install"
},
{
"text": "常见命令",
"link": "03-bash"
},
{