docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/allenai/olmocr) · [上游 README](https://github.com/allenai/olmocr/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <div align="center">
   <img width="350" alt="olmocr-2-full@2x" src="https://github.com/user-attachments/assets/24f1b596-4059-46f1-8130-5d72dcc0b02e" />
 <hr/>
@@ -23,34 +29,33 @@
   </a>
 </p>
 
-A toolkit for converting PDFs and other image-based document formats into clean, readable, plain text format.
+一款将 PDF 及其他基于图像的文档格式转换为干净、可读纯文本格式的工具包。
 
-Try the online demo: [https://olmocr.allenai.org/](https://olmocr.allenai.org/)
+试用在线演示：[https://olmocr.allenai.org/](https://olmocr.allenai.org/)
 
-Features:
- - Convert PDF, PNG, and JPEG based documents into clean Markdown
- - Support for equations, tables, handwriting, and complex formatting
- - Automatically removes headers and footers
- - Convert into text with a natural reading order, even in the presence of
-   figures, multi-column layouts, and insets
- - Efficient, less than $200 USD per million pages converted
- - (Based on a 7B parameter VLM, so it requires a GPU)
+功能特性：
+ - 将基于 PDF、PNG 和 JPEG 的文档转换为干净的 Markdown
+ - 支持公式、表格、手写体及复杂排版
+ - 自动移除页眉和页脚
+ - 即使存在插图、多栏布局和嵌入内容，也能按自然阅读顺序转换为文本
+ - 高效，每转换一百万页成本低于 200 美元
+ - （基于 70 亿参数 VLM（Vision Language Model，视觉语言模型），因此需要 GPU）
 
-### News
- - October 21, 2025 - v0.4.0 - [New model release](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8), boosts olmOCR-bench score by ~4 points using synthetic data and introduces RL training.
- - August 13, 2025 - v0.3.0 - [New model release](https://huggingface.co/allenai/olmOCR-7B-0825-FP8), fixes auto-rotation detection, and hallucinations on blank documents.
- - July 24, 2025 - v0.2.1 - [New model release](https://huggingface.co/allenai/olmOCR-7B-0725-FP8), scores 3 points higher on [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/olmocr/bench), also runs significantly faster because it's default FP8, and needs much fewer retries per document.
- - July 23, 2025 - v0.2.0 - New cleaned up [trainer code](https://github.com/allenai/olmocr/tree/main/olmocr/train), makes it much simpler to train olmOCR models yourself.
- - June 17, 2025 - v0.1.75 - Switch from sglang to vllm based inference pipeline, updated docker image to CUDA 12.8.
- - May 23, 2025 - v0.1.70 - Official docker support and images are now available! [See Docker usage](#using-docker)
- - May 19, 2025 - v0.1.68 - [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/olmocr/bench) launch, scoring 77.4. Launch includes 2 point performance boost in olmOCR pipeline due to bug fixes with prompts.
- - Mar 17, 2025 - v0.1.60 - Performance improvements due to better temperature selection in sampling.
- - Feb 25, 2025 - v0.1.58 -  Initial public launch and demo.
+### 新闻
+ - 2025 年 10 月 21 日 - v0.4.0 - [新模型发布](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8), 通过合成数据将 olmOCR-bench 分数提升约 4 分，并引入 RL（Reinforcement Learning，强化学习）训练。
+ - 2025 年 8 月 13 日 - v0.3.0 - [新模型发布](https://huggingface.co/allenai/olmOCR-7B-0825-FP8), 修复了自动旋转检测及空白文档上的幻觉问题。
+ - 2025 年 7 月 24 日 - v0.2.1 - [新模型发布](https://huggingface.co/allenai/olmOCR-7B-0725-FP8), 在 [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/olmocr/bench), 上得分提高 3 分，同时由于默认采用 FP8，运行速度显著加快，且每份文档所需重试次数大幅减少。
+ - 2025 年 7 月 23 日 - v0.2.0 - 全新整理的 [训练代码](https://github.com/allenai/olmocr/tree/main/olmocr/train), 使自行训练 olmOCR 模型变得更加简单。
+ - 2025 年 6 月 17 日 - v0.1.75 - 推理流水线从 sglang 切换为基于 vLLM 的实现，Docker 镜像更新至 CUDA 12.8。
+ - 2025 年 5 月 23 日 - v0.1.70 - 官方 Docker 支持及镜像现已可用！[查看 Docker 用法](#using-docker)
+ - 2025 年 5 月 19 日 - v0.1.68 - [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/olmocr/bench) 发布，得分 77.4。发布版本因提示词相关 bug 修复，olmOCR 流水线性能提升 2 分。
+ - 2025 年 3 月 17 日 - v0.1.60 - 通过改进采样温度选择提升性能。
+ - 2025 年 2 月 25 日 - v0.1.58 - 首次公开发布及演示。
 
-### Benchmark
+### 基准测试
 
 [**olmOCR-Bench**](https://github.com/allenai/olmocr/tree/main/olmocr/bench):
-We also ship a comprehensive benchmark suite covering over 7,000 test cases across 1,400 documents to help measure performance of OCR systems. 
+我们还提供一套全面的基准测试套件，涵盖 1,400 份文档中的 7,000 多个测试用例，用于衡量 OCR 系统的性能。
 
 <table>
     <thead>
@@ -183,45 +188,44 @@ We also ship a comprehensive benchmark suite covering over 7,000 test cases acro
 </table>
 
 
-### Installation
+### 安装
 
-#### System Dependencies
+#### 系统依赖
 
-You will need to install poppler-utils and additional fonts for rendering PDF images.
+你需要安装 poppler-utils 以及用于渲染 PDF 图像的额外字体。
 
-Install dependencies (Ubuntu/Debian):
+安装依赖（Ubuntu/Debian）：
 ```bash
 sudo apt-get update
 sudo apt-get install poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools
 ```
 
-#### Python Installation
+#### Python 安装
 
-Set up a conda environment and install olmocr. The requirements for running olmOCR
-are difficult to install in an existing python environment, so please do make a clean python environment to install into.
+设置 conda 环境并安装 olmocr。运行 olmOCR 的依赖项在现有 Python 环境中较难安装，因此请务必创建一个干净的 Python 环境进行安装。
 
 ```bash
 conda create -n olmocr python=3.11
 conda activate olmocr
 ```
 
-Choose the installation option that matches your use case:
+选择与你的使用场景匹配的安装选项：
 
-**Option 1: Remote Inference (Lightweight)**
+**选项 1：远程推理（轻量）**
 
-If you plan to use a remote vLLM server with the `--server` flag, install the base package:
+如果你计划配合 `--server` 标志使用远程 vLLM 服务器，请安装基础包：
 ```bash
 pip install olmocr
 ```
-This avoids installing heavy GPU dependencies like PyTorch (~2GB+).
+这可避免安装 PyTorch（约 2GB+）等重型 GPU 依赖。
 
-**Option 2: Local GPU Inference**
+**选项 2：本地 GPU 推理**
 
-Requirements:
- - Recent NVIDIA GPU (tested on RTX 4090, L40S, A100, H100) with at least 12 GB of GPU RAM
- - 30GB of free disk space
+要求：
+ - 较新的 NVIDIA GPU（已在 RTX 4090、L40S、A100、H100 上测试），至少 12 GB GPU 显存
+ - 30GB 可用磁盘空间
 
-For running inference with your own GPU:
+使用自有 GPU 运行推理：
 ```bash
 pip install olmocr[gpu] --extra-index-url https://download.pytorch.org/whl/cu128
 
@@ -229,23 +233,23 @@ pip install olmocr[gpu] --extra-index-url https://download.pytorch.org/whl/cu128
 pip install https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl
 ```
 
-**Option 3: Beaker Cluster Execution**
+**选项 3：Beaker 集群执行**
 
-For submitting jobs to Beaker clusters with the `--beaker` flag:
+用于向 Beaker 集群提交作业，并配合 `--beaker` 标志：
 ```bash
 pip install olmocr[beaker]
 ```
 
-**Option 4: Benchmark Suite**
+**选项 4：基准测试套件**
 
-For running the olmOCR benchmark suite:
+用于运行 olmOCR 基准测试套件：
 ```bash
 pip install olmocr[bench]
 ```
 
-**Combined Installation**
+**组合安装**
 
-You can combine multiple options:
+你可以组合多个选项：
 ```bash
 # GPU + Beaker support
 pip install olmocr[gpu,beaker] --extra-index-url https://download.pytorch.org/whl/cu128
@@ -254,18 +258,18 @@ pip install olmocr[gpu,beaker] --extra-index-url https://download.pytorch.org/wh
 pip install olmocr[gpu,bench] --extra-index-url https://download.pytorch.org/whl/cu128
 ```
 
-**Troubleshooting**
+**故障排查**
 
-If you run into errors about `too many open files`, update your ulimit:
+如果遇到与 `too many open files` 相关的错误，请更新 ulimit：
 ```bash
 ulimit -n 65536
 ```
 
-### Usage Examples
+### 使用示例
 
-For quick testing, try the [web demo](https://olmocr.allen.ai/).
+如需快速测试，可尝试 [web demo](https://olmocr.allen.ai/).
 
-**Convert a Single PDF (Local GPU):**
+**转换单个 PDF（本地 GPU）：**
 ```bash
 # Download a sample PDF
 curl -o olmocr-sample.pdf https://olmocr.allenai.org/papers/olmocr_3pg_sample.pdf
@@ -274,28 +278,28 @@ curl -o olmocr-sample.pdf https://olmocr.allenai.org/papers/olmocr_3pg_sample.pd
 olmocr ./localworkspace --markdown --pdfs olmocr-sample.pdf
 ```
 
-**Convert an Image file:**
+**转换图像文件：**
 ```bash
 olmocr ./localworkspace --markdown --pdfs random_page.png
 ```
 
-**Convert Multiple PDFs:**
+**转换多个 PDF：**
 ```bash
 olmocr ./localworkspace --markdown --pdfs tests/gnarly_pdfs/*.pdf
 ```
 
-**Use Remote Inference Server:**
+**使用远程推理服务器：**
 ```bash
 olmocr ./localworkspace --server http://remote-server:8000/v1 --model allenai/olmOCR-2-7B-1025-FP8 --markdown --pdfs *.pdf
 ```
 
-With the `--markdown` flag, results will be stored as markdown files inside of `./localworkspace/markdown/`.
+配合 `--markdown` 标志，结果将以 markdown 文件形式保存在 `./localworkspace/markdown/` 内。
 
-> **Note:** You can also use `python -m olmocr.pipeline` instead of `olmocr` if you prefer. 
+> **注意：** 如果你愿意，也可以使用 `python -m olmocr.pipeline` 代替 `olmocr`。
 
-#### Viewing Results
+#### 查看结果
 
-The `./localworkspace/` workspace folder will then have both [Dolma](https://github.com/allenai/dolma) and markdown files (if using `--markdown`).
+随后，`./localworkspace/` 工作区文件夹将同时包含 [Dolma](https://github.com/allenai/dolma) 和 markdown 文件（若使用 `--markdown`）。
 
 
 ```bash
@@ -307,32 +311,32 @@ olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
 ...
 ```
 
-### Using an Inference Provider or External Server
+### 使用推理提供商或外部服务器
 
-If you have a vLLM server already running elsewhere (or any inference platform implementing the OpenAI API), you can point olmOCR to use it instead of spawning a local instance.
+如果你已在其他地方运行 vLLM 服务器（或任何实现 OpenAI API 的推理平台），可以让 olmOCR 指向它，而无需在本地启动实例。
 
-**Installation for Remote Inference:**
+**远程推理安装：**
 ```bash
 # Lightweight installation - no GPU dependencies needed
 pip install olmocr
 ```
 
-**Using an External Server:**
+**使用外部服务器：**
 ```bash
 # Use external vLLM server instead of local one
 olmocr ./localworkspace --server http://remote-server:8000/v1 --model allenai/olmOCR-2-7B-1025-FP8 --markdown --pdfs tests/gnarly_pdfs/*.pdf
 ```
 
-The served model name in vLLM needs to match the value provided in `--model`.
+vLLM 中提供的模型名称必须与 `--model` 中提供的值一致。
 
-**Example vLLM Server Launch:**
+**vLLM 服务器启动示例：**
 ```bash
 vllm serve allenai/olmOCR-2-7B-1025-FP8 --max-model-len 16384
 ```
 
-#### Verified External Providers
+#### 已验证的外部提供商
 
-We have tested `olmOCR-2-7B-1025-FP8` on these external model providers and confirmed that they work
+我们已在以下外部模型提供商上测试 `olmOCR-2-7B-1025-FP8`，并确认其可用
 
 |                                                                             | $/1M Input tokens | $/1M Output tokens | Example Command                                                                                                                                                                |
 |-----------------------------------------------------------------------------|-------------------|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -341,65 +345,65 @@ We have tested `olmOCR-2-7B-1025-FP8` on these external model providers and conf
 | [Parasail](https://www.saas.parasail.io/serverless?name=olmocr-7b-1025-fp8) | $0.10             | $0.20              | `olmocr ./workspace --server https://api.parasail.io/v1 --api_key psk-XXXXX --workers 1 --max_concurrent_requests 20 --model allenai/olmOCR-2-7B-1025 --pdfs tests/gnarly_pdfs/*.pdf`          |
 
 
-Notes on arguments
-- `--server`: Defines the OpenAI-compatible endpoint: ex `https://api.deepinfra.com/v1/openai`
-- `--api_key`: Your API key, bassed in via Authorization Bearer HTTP header
-- `--max_concurrent_requests`: Max concurrent requests that will be in-flight to the inference provider at one time
-- `--workers`: Max number of page groups that will be processed at once. You may want to set this to `1` so that you finish one group of stuff before moving on.
-- `--pages_per_group`: You may want a smaller number of pages per group as many external provides have lower concurrent request limits
-- `--model`: The model identifier, ex. `allenai/olmOCR-2-7B-1025`, different providers have different names, and if you run locally, you can use `olmocr`
-- Other arguments work the same as with local inference
+参数说明
+- `--server`：定义 OpenAI 兼容端点，例如 `https://api.deepinfra.com/v1/openai`
+- `--api_key`：你的 API 密钥，通过 Authorization Bearer HTTP 标头传入
+- `--max_concurrent_requests`：同时发往推理提供商的在途请求最大并发数
+- `--workers`：一次处理的最大页面组数量。你可能希望将其设为 `1`，以便在处理下一组之前先完成当前一组。
+- `--pages_per_group`：你可能希望每组包含更少的页数，因为许多外部提供商的并发请求上限较低
+- `--model`：模型标识符，例如 `allenai/olmOCR-2-7B-1025`；不同提供商的名称各不相同，若在本地运行，可使用 `olmocr`
+- 其他参数与本地推理时的用法相同
 
 
-### Multi-node / Cluster Usage
+### 多节点 / 集群用法
 
-If you want to convert millions of PDFs using multiple nodes running in parallel, olmOCR supports
-reading PDFs from AWS S3 and coordinating work using an AWS S3 output bucket.
+如果你想使用多个并行运行的节点将数百万份 PDF 进行转换，olmOCR 支持
+从 AWS S3 读取 PDF，并使用 AWS S3 输出存储桶协调任务。
 
-**Start the first worker node:**
+**启动第一个工作节点：**
 ```bash
 olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf
 ```
 
-This sets up a simple work queue in your AWS bucket and starts converting PDFs.
+这会在你的 AWS 存储桶中建立一个简单的工作队列，并开始转换 PDF。
 
-**On subsequent worker nodes:**
+**在后续工作节点上：**
 ```bash
 olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace
 ```
 
-They will automatically start grabbing items from the same workspace queue.
+它们会自动从同一工作区队列中获取任务。
 
-#### Using Beaker for Cluster Execution
+#### 使用 Beaker 进行集群执行
 
-If you are at Ai2 and want to linearize millions of PDFs efficiently using [beaker](https://www.beaker.org), install with Beaker support:
+如果你在 Ai2，并希望借助 [beaker](https://www.beaker.org), 高效地将数百万份 PDF 线性化，请安装带 Beaker 支持的版本：
 
 ```bash
 pip install olmocr[gpu,beaker] --extra-index-url https://download.pytorch.org/whl/cu128
 ```
 
-Then use the `--beaker` flag to prepare the workspace locally and launch N GPU workers in the cluster:
+然后使用 `--beaker` 标志在本地准备工作区，并在集群中启动 N 个 GPU 工作节点：
 
 ```bash
 olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf --beaker --beaker_gpus 4
 ```
 
 
-### Using Docker
+### 使用 Docker
 
-Pull the Docker image (large, includes the model, ~30GB):
+拉取 Docker 镜像（体积较大，包含模型，约 30GB）：
 ```bash
 docker pull alleninstituteforai/olmocr:latest-with-model
 ```
 
-For advanced users who want to manage their own model downloads, we also provide a base image without the model:
+对于希望自行管理模型下载的高级用户，我们还提供不含模型的基础镜像：
 ```bash
 docker pull alleninstituteforai/olmocr:latest
 ```
 
-#### Quick Start - Process PDFs
+#### 快速开始 - 处理 PDF
 
-Process a single PDF in your current directory:
+处理当前目录中的单个 PDF：
 ```bash
 docker run --gpus all \
   -v $(pwd):/workspace \
@@ -407,7 +411,7 @@ docker run --gpus all \
   -c "olmocr /workspace/output --markdown --pdfs /workspace/sample.pdf"
 ```
 
-Process multiple PDFs:
+处理多个 PDF：
 ```bash
 docker run --gpus all \
   -v /path/to/pdfs:/input \
@@ -416,18 +420,18 @@ docker run --gpus all \
   -c "olmocr /output --markdown --pdfs /input/*.pdf"
 ```
 
-#### Interactive Mode
+#### 交互模式
 
-Run the container interactively for exploration and debugging:
+以交互方式运行容器，便于探索和调试：
 ```bash
 docker run -it --gpus all alleninstituteforai/olmocr:latest-with-model
 ```
 
-> Visit our Docker repository on [Docker Hub](https://hub.docker.com/r/alleninstituteforai/olmocr) for more information.
+> 访问我们在 [Docker Hub](https://hub.docker.com/r/alleninstituteforai/olmocr) 上的 Docker 仓库以了解更多信息。
 
-### Full Documentation
+### 完整文档
 
-To see all available options:
+要查看所有可用选项：
 ```bash
 olmocr --help
 usage: pipeline.py [-h] [--pdfs [PDFS ...]] [--model MODEL] [--workspace_profile WORKSPACE_PROFILE] [--pdf_profile PDF_PROFILE] [--pages_per_group PAGES_PER_GROUP] [--max_page_retries MAX_PAGE_RETRIES] [--max_page_error_rate MAX_PAGE_ERROR_RATE] [--workers WORKERS]
@@ -490,41 +494,41 @@ beaker/cluster execution:
                         Beaker priority level for the job
 ```
 
-## Code overview
+## 代码概览
 
-There are some nice reusable pieces of the code that may be useful for your own projects:
- - A prompting strategy to get really good natural text parsing using ChatGPT 4o - [buildsilver.py](https://github.com/allenai/olmocr/blob/main/olmocr/data/buildsilver.py)
- - Basic filtering by language and SEO spam removal - [filter.py](https://github.com/allenai/olmocr/blob/main/olmocr/filter/filter.py)
- - SFT Finetuning code for Qwen2.5-VL - [train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/train.py)
- - GRPO RL Trainer - [grpo_train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/grpo_train.py)
- - Synthetic data generation - [mine_html_templates.py](https://github.com/allenai/olmocr/blob/main/olmocr/synth/mine_html_templates.py)
- - Processing millions of PDFs through a finetuned model using VLLM - [pipeline.py](https://github.com/allenai/olmocr/blob/main/olmocr/pipeline.py)
- - Viewing [Dolma docs](https://github.com/allenai/dolma) created from PDFs - [dolmaviewer.py](https://github.com/allenai/olmocr/blob/main/olmocr/viewer/dolmaviewer.py)
+其中有一些很好的可复用代码片段，也许对你自己的项目有帮助：
+ - 一种使用 ChatGPT 4o 获得高质量自然语言文本解析的提示策略（prompting strategy）- [buildsilver.py](https://github.com/allenai/olmocr/blob/main/olmocr/data/buildsilver.py)
+ - 按语言进行基础过滤并去除 SEO 垃圾内容 - [filter.py](https://github.com/allenai/olmocr/blob/main/olmocr/filter/filter.py)
+ - Qwen2.5-VL 的 SFT 微调代码 - [train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/train.py)
+ - GRPO 强化学习（RL）训练器 - [grpo_train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/grpo_train.py)
+ - 合成数据生成 - [mine_html_templates.py](https://github.com/allenai/olmocr/blob/main/olmocr/synth/mine_html_templates.py)
+ - 使用 VLLM 通过微调模型处理数百万份 PDF - [pipeline.py](https://github.com/allenai/olmocr/blob/main/olmocr/pipeline.py)
+ - 查看由 PDF 生成的 [Dolma 文档](https://github.com/allenai/dolma) - [dolmaviewer.py](https://github.com/allenai/olmocr/blob/main/olmocr/viewer/dolmaviewer.py)
 
 
 
-## Team
+## 团队
 
 <!-- start team -->
 
-**olmOCR** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/).
-AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
-To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/olmocr/graphs/contributors) page.
+**olmOCR** 由 AllenNLP 团队开发并维护，得到 [艾伦人工智能研究所（Allen Institute for Artificial Intelligence，AI2）](https://allenai.org/). 的支持
+AI2 是一家非营利研究机构，使命是通过高影响力的 AI 研究与工程为人类作出贡献。
+若要了解具体有哪些人为本代码库作出贡献，请参阅[我们的贡献者](https://github.com/allenai/olmocr/graphs/contributors) 页面。
 
 <!-- end team -->
 
-## License
+## 许可证
 
 <!-- start license -->
 
-**olmOCR** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
-A full copy of the license can be found [on GitHub](https://github.com/allenai/olmocr/blob/main/LICENSE).
+**olmOCR** 采用 [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). 许可证
+完整许可证副本可在 [GitHub](https://github.com/allenai/olmocr/blob/main/LICENSE). 上找到
 
 <!-- end license -->
 
-## Citing
+## 引用
 
-For olmOCR v1 and OlmOCR-bench:
+引用 olmOCR v1 与 OlmOCR-bench：
 ```bibtex
 @misc{olmocrbench,
       title={{olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models}},
@@ -537,7 +541,7 @@ For olmOCR v1 and OlmOCR-bench:
 }
 ```
 
-For olmOCR v2 Unit Testing Rewards with RL:
+引用 olmOCR v2 Unit Testing Rewards with RL：
 ```bibtex
 @misc{olmocr2,
       title={olmOCR 2: Unit Test Rewards for Document OCR},