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<!DOCTYPE html>
<html lang="en">
<head>
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<title>Chinese reading task about ML</title>
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<body>
<div class="container">
<h1>MLGym: A New Framework and Benchmark for Advancing AI Research Agents</h1>
<div><p class='zh-text'>1. 我们介绍了Meta MLGym和MLGym-Bench，这是一个用于评估和开发LLM代理在AI研究任务上的新框架和基准。</p>
<p class='zh-text'>2. 这是第一个用于机器学习任务的Gym环境，支持强化学习算法的研究。</p>
<p class='zh-text'>3. MLGym-Bench包含13个多样且开放的AI研究任务，涵盖计算机视觉、自然语言处理、强化学习和博弈论等领域。</p>
<p class='zh-text'>4. 解决这些任务需要实际的AI研究技能，如产生新想法和假设、创建和处理数据、实现ML方法、训练模型、运行实验、分析结果并迭代改进任务。</p>
<p class='zh-text'>5. 我们评估了多个前沿大语言模型，如Claude-3.5-Sonnet和GPT-4o。</p>
<p class='zh-text'>6. 我们的MLGym框架使得添加新任务、集成和评估模型、大规模生成合成数据以及开发新的学习算法变得容易。</p>
<p class='zh-text'>7. 我们发现当前的前沿模型可以通过找到更好的超参数来改进基线，但不会产生新的假设、算法、架构或显著改进。</p>
<p class='zh-text'>8. 我们开源了我们的框架和基准，以促进未来在提高LLM代理的AI研究能力方面的研究。</p></div>
<div class="pinyin">
<p>1. Wǒmen jièshào le Meta MLGym hé MLGym-Bench, zhè shì yīgè yòngyú pínggǔ hé kāifā LLM dàilǐ zài AI yánjiū rènwù shàng de xīn kuàngjià hé jīzhǔn</p>
<p>2. Zhè shì dì-yīgè yòngyú jīqǐ xuéxí rènwù de Gym huánjìng, zhīchí qiángzhù xuéxí suànfǎ de yánjiū</p>
<p>3. MLGym-Bench bāohán 13 gè duōyàng qiě kāifàng de AI yánjiū rènwù, hánfù jìsuànjī shìjiè, zìrán yǔyán chǔlǐ, qiángzhù xuéxí hé bóyìlùn děng lǐngyù</p>
<p>4. Jiějué zhèxiē rènwù xūyào shíjì de AI yánjiū jìnéng, rú chǎnshēng xīn yìxiǎng hé jiǎshè, chuàngjiàn hé chǔlǐ shùjù, shíxiàn ML fāngfǎ, xùnliàn móxíng, yùnxíng shìyàn, fēnxi jiéguǒ bìng diédǎi gǎijìn rènwù</p>
<p>5. Wǒmen pínggǔ le duō gè qiánxīn dà yǔyán móxíng, rú Claude-3</p>
<p>6. 5-Sonnet hé GPT-4o</p>
<p>7. Wǒmen de MLGym kuàngjià shǐdé tiānjiā xīn rènwù, jíchéng hé pínggǔ móxíng, dàguīmó shēngchéng héchéng shùjù yǐjí kāifā xīn de xuéxí suànfǎ biàndé róngyì</p>
<p>8. Wǒmen fāxiàn dāngqián de qiánxīn móxíng kěyǐ tōngguò zhǎodào gèng hǎo de chāocānshù lái gǎijìn jīchǔ, dàn bùhuì chǎnshēng xīn de jiǎshè, suànfǎ, jiàgòu huò xiǎnzhù gǎijìn</p>
<p>9. Wǒmen kāiyuán le wǒmen de kuàngjià hé jīzhǔn, yǐ cùjìn wèilǎi zài tígāo LLM dàilǐ de AI yánjiū nénglì fāngmiàn de yánjiū</p>
</div>
<div><p>1. We introduced Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks.</p>
<p>2. This is the first Gym environment for machine learning tasks that supports research on reinforcement learning algorithms.</p>
<p>3. MLGym-Bench includes 13 diverse and open AI research tasks, covering areas such as computer vision, natural language processing, reinforcement learning, and game theory.</p>
<p>4. Solving these tasks requires practical AI research skills, such as generating new ideas and hypotheses, creating and handling data, implementing ML methods, training models, running experiments, analyzing results, and iteratively improving tasks.</p>
<p>5. We evaluated several state-of-the-art large language models, such as Claude-3.</p>
<p>6. 5-Sonnet and GPT-4o.</p>
<p>7. Our MLGym framework makes it easy to add new tasks, integrate and evaluate models, generate synthetic data at scale, and develop new learning algorithms.</p>
<p>8. We found that current state-of-the-art models can improve baselines by finding better hyperparameters but do not generate new hypotheses, algorithms, architectures, or significant improvements.</p>
<p>9. We have open-sourced our framework and benchmark to promote future research in enhancing the AI research capabilities of LLM agents.</p></div>
<h2>Vocabulary</h2>
<table>
<thead>
<tr>
<th>Word</th>
<th>Pinyin</th>
<th>Translation</th>
</tr>
</thead>
<tbody>
<tr>
<td class="zh">介绍</td>
<td>jiè shào</td>
<td>introduce</td>
</tr>
<tr>
<td class="zh">框架</td>
<td>kuàng jià</td>
<td>framework</td>
</tr>
<tr>
<td class="zh">基准</td>
<td>jī zhǔn</td>
<td>benchmark</td>
</tr>
<tr>
<td class="zh">评估</td>
<td>píng gū</td>
<td>evaluate</td>
</tr>
<tr>
<td class="zh">开发</td>
<td>kāi fā</td>
<td>develop</td>
</tr>
<tr>
<td class="zh">代理</td>
<td>dài lǐ</td>
<td>agent</td>
</tr>
<tr>
<td class="zh">任务</td>
<td>rèn wu</td>
<td>task</td>
</tr>
<tr>
<td class="zh">支持</td>
<td>zhī chí</td>
<td>support</td>
</tr>
<tr>
<td class="zh">强化</td>
<td>qiáng huà</td>
<td>reinforce</td>
</tr>
<tr>
<td class="zh">算法</td>
<td>suàn fǎ</td>
<td>algorithm</td>
</tr>
<tr>
<td class="zh">多样</td>
<td>duō yàng</td>
<td>diverse</td>
</tr>
<tr>
<td class="zh">开放</td>
<td>kāi fàng</td>
<td>open</td>
</tr>
<tr>
<td class="zh">涵盖</td>
<td>hán gài</td>
<td>cover</td>
</tr>
<tr>
<td class="zh">计算机视觉</td>
<td>jì suàn jī shì jué</td>
<td>computer vision</td>
</tr>
<tr>
<td class="zh">自然语言处理</td>
<td>zì rán yǔ yán chǔ lǐ</td>
<td>natural language processing</td>
</tr>
<tr>
<td class="zh">博弈论</td>
<td>bó yì lùn</td>
<td>game theory</td>
</tr>
<tr>
<td class="zh">技能</td>
<td>jì néng</td>
<td>skill</td>
</tr>
<tr>
<td class="zh">假设</td>
<td>jiǎ shè</td>
<td>hypothesis</td>
</tr>
<tr>
<td class="zh">创建</td>
<td>chuàng jiàn</td>
<td>create</td>
</tr>
<tr>
<td class="zh">处理</td>
<td>chǔ lǐ</td>
<td>process</td>
</tr>
<tr>
<td class="zh">实现</td>
<td>shí xiàn</td>
<td>implement</td>
</tr>
<tr>
<td class="zh">训练</td>
<td>xùn liàn</td>
<td>train</td>
</tr>
<tr>
<td class="zh">实验</td>
<td>shí yàn</td>
<td>experiment</td>
</tr>
<tr>
<td class="zh">分析</td>
<td>fēn xī</td>
<td>analyze</td>
</tr>
<tr>
<td class="zh">迭代</td>
<td>dié dài</td>
<td>iterate</td>
</tr>
<tr>
<td class="zh">改进</td>
<td>gǎi jìn</td>
<td>improve</td>
</tr>
<tr>
<td class="zh">前沿</td>
<td>qián yán</td>
<td>frontier</td>
</tr>
<tr>
<td class="zh">超参数</td>
<td>chāo cān shù</td>
<td>hyperparameter</td>
</tr>
<tr>
<td class="zh">基线</td>
<td>jī xiàn</td>
<td>baseline</td>
</tr>
<tr>
<td class="zh">生成</td>
<td>shēng chéng</td>
<td>generate</td>
</tr>
<tr>
<td class="zh">合成</td>
<td>hé chéng</td>
<td>synthetic</td>
</tr>
<tr>
<td class="zh">开源</td>
<td>kāi yuán</td>
<td>open source</td>
</tr>
<tr>
<td class="zh">促进</td>
<td>cù jìn</td>
<td>promote</td>
</tr>
<tr>
<td class="zh">能力</td>
<td>néng lì</td>
<td>ability</td>
</tr>
</tbody>
</table>
</div>
</body>
</html>