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<!DOCTYPE html>
<html lang="en">
<head>
<script async src="https://www.googletagmanager.com/gtag/js?id=G-C1CRWDNJ1J"></script>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Chinese reading task about ML</title>
<style>
body {
font-family: Arial, sans-serif;
background-color: #f4f4f9;
color: #333;
margin: 0;
padding: 20px;
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.container {
max-width: 800px;
margin: 0 auto;
background-color: #fff;
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.zh-text {
font-size: 1.3em;
font-family: 'Noto Sans SC';
font-weight: 300;
margin: 0 0 5px 0;
}
.pinyin {
padding-top: 5px;
padding-bottom: 5px;
font-style: italic;
color: #888;
}
table {
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margin-top: 20px;
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background-color: #f9f9f9;
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td.zh {
font-family: 'Noto Sans SC';
font-size: 1.2em;
font-weight: 400;
}
</style>
</head>
<body>
<div class="container">
<h1>Visual-RFT: Visual Reinforcement Fine-Tuning</h1>
<div><p class='zh-text'>1. 这篇文章介绍了一种叫做视觉强化微调（Visual-RFT）的方法。</p>
<p class='zh-text'>2. 它用于改进大型视觉-语言模型（LVLMs）在视觉任务中的表现。</p>
<p class='zh-text'>3. 这种方法通过生成多个答案并使用可验证的奖励函数更新模型。</p>
<p class='zh-text'>4. 实验结果显示，Visual-RFT在多个视觉任务中表现出色，优于监督微调（SFT）。</p>
<p class='zh-text'>5. 例如，在细粒度图像分类中，Visual-RFT提高了24.3%的准确率。</p></div>
<div class="pinyin">
<p>1. 这篇文章介绍了一种叫做视觉强化微调（Visual-RFT）的方法。
Zhè piān wénzhāng jièshào le yī zhǒng jiào zuò shìjué qiángzhù wēitiáo （Visual-RFT）de fāngfǎ</p>
<p>2.
它用于改进大型视觉-语言模型（LVLMs）在视觉任务中的表现。
Tā yòngyú gǎijìn dàxíng shìjué-yǔyán móxíng （LVLMs） zài shìjué rènwù zhōng de biǎoxiàn</p>
<p>3.
这种方法通过生成多个答案并使用可验证的奖励函数更新模型。
Zhè zhǒng fāngfǎ tōngguò shēngchéng duō gè dá'àn bìng shǐyòng kě yànzhèng de jiǎnglì hánshù gēngxīn móxíng</p>
<p>4.
实验结果显示，Visual-RFT在多个视觉任务中表现出色，优于监督微调（SFT）。
Shíyàn jiéguǒ xiǎnshì, Visual-RFT zài duō gè shìjué rènwù zhōng biǎoxiàn chūsè, yōuyú jiàndū wēitiáo （SFT）</p>
<p>5.
例如，在细粒度图像分类中，Visual-RFT提高了24</p>
<p>6. 3%的准确率。
Lìrú, zài xìlìdù túxiàng fēnlèi zhōng, Visual-RFT tígāo le 24</p>
<p>7. 3% de zhǔnquèlǜ</p>
</div>
<div><p>1. This article introduces a method called Visual Reinforcement Fine-Tuning (Visual-RFT).</p>
<p>2. It is used to improve the performance of large vision-language models (LVLMs) in visual tasks.</p>
<p>3. This method works by generating multiple answers and updating the model using a verifiable reward function.</p>
<p>4. Experimental results show that Visual-RFT performs excellently in multiple visual tasks, outperforming supervised fine-tuning (SFT).</p>
<p>5. For example, in fine-grained image classification, Visual-RFT improved accuracy by 24.</p>
<p>6. 3%.</p></div>
<h2>Vocabulary</h2>
<table>
<thead>
<tr>
<th>Word</th>
<th>Pinyin</th>
<th>Translation</th>
</tr>
</thead>
<tbody>
<tr>
<td class="zh">视觉强化微调</td>
<td>shìjué qiángzhù wēitiáo</td>
<td>visual reinforcement fine-tuning</td>
</tr>
<tr>
<td class="zh">大型视觉-语言模型</td>
<td>dàxíng shìjué-yǔyán móxíng</td>
<td>large vision-language models</td>
</tr>
<tr>
<td class="zh">表现</td>
<td>biǎoxiàn</td>
<td>performance</td>
</tr>
<tr>
<td class="zh">生成</td>
<td>shēngchéng</td>
<td>generate</td>
</tr>
<tr>
<td class="zh">可验证的</td>
<td>kě yànzhèng de</td>
<td>verifiable</td>
</tr>
<tr>
<td class="zh">奖励函数</td>
<td>jiǎnglì hánshù</td>
<td>reward function</td>
</tr>
<tr>
<td class="zh">更新</td>
<td>gēngxīn</td>
<td>update</td>
</tr>
<tr>
<td class="zh">实验结果</td>
<td>shíyàn jiéguǒ</td>
<td>experimental results</td>
</tr>
<tr>
<td class="zh">出色</td>
<td>chūsè</td>
<td>outstanding</td>
</tr>
<tr>
<td class="zh">优于</td>
<td>yōu yú</td>
<td>superior to</td>
</tr>
<tr>
<td class="zh">监督微调</td>
<td>jiàndū wēitiáo</td>
<td>supervised fine-tuning</td>
</tr>
<tr>
<td class="zh">细粒度</td>
<td>xì lìdù</td>
<td>fine-grained</td>
</tr>
<tr>
<td class="zh">图像分类</td>
<td>túxiàng fēnlèi</td>
<td>image classification</td>
</tr>
<tr>
<td class="zh">准确率</td>
<td>zhǔnquèlǜ</td>
<td>accuracy</td>
</tr>
</tbody>
</table>
</div>
</body>
</html>