Update README: Merge experimental setup into results section

README.md

@@ -133,16 +133,12 @@ Natural web data is mostly declarative text, lacking structured reasoning steps
 
 ## 📈 Experimental Results
 
-We used the **MiniCPM-1.2B** model architecture and **MiniCPM3-4B** tokenizer for experimental verification. Each experiment was conducted with a training volume of **100 billion Tokens**, using the **Decay Verification** method (annealing from a 1.3T base model). We used [OpenCompass](https://github.com/open-compass/opencompass) as our evaluation framework. Evaluation benchmarks include:
+We evaluated data quality using the **Decay Verification** method: continuing pre-training of a **MiniCPM-1.2B** base model (pre-trained on 1.3T tokens with **MiniCPM3-4B** tokenizer) with **~100B tokens** (30% target data + 70% general data). We used [OpenCompass](https://github.com/open-compass/opencompass) as our evaluation framework. Evaluation benchmarks include:
 
 - **Mathematical Reasoning:** GSM8K, MATH500, Math-Bench, R-Bench-Math
 - **Code Generation:** HumanEval, MBPP
 - **Comprehensive Knowledge:** MMLU, MMLU-STEM
 
-### 🔧 Experimental Setup
-
-We evaluated data quality using the **Decay Verification** method: continuing pre-training of a **MiniCPM-1.2B** base model (pre-trained on 1.3T tokens) with **~100B tokens** (30% target data + 70% general data). 
-
 ### Effectiveness of L0 Parsing Strategy
 
 To fairly compare different parsing strategies, we conducted experiments on a data subset sampled from the **2023-2024** distribution. We re-parsed the raw HTML from this source using different parsers and **applied the same L1 cleaning operators to all baselines**. This comparison demonstrates the **overall benefit of our L0 Parser + L1 Filtering pipeline** against other parsers under identical cleaning conditions.
@@ -193,7 +189,7 @@ If you find **UltraData-Math** useful in your research, please consider citing:
 ```bibtex
 @misc{ultradata-math,
   title={UltraData-Math},
-  author={Chuyue Zhou and Hongya Lv and Xinle Lin and Yudong Wang and Jie Zhou and Hengyu Zhao and Junshao Guo and Xueren Zhang and Shuaikang Xue and Zhiyuan Liu},
+  author={UltraData Team},
   year={2026},
   url={https://huggingface.co/datasets/openbmb/UltraData-Math},
   publisher={Hugging Face}

README_ZH.md

@@ -98,16 +98,12 @@ L1 数据虽然格式整洁，但内容质量参差不齐。L2 阶段引入了
 
 ## 📈 实验结果
 
-我们采用 **MiniCPM-1.2B** 模型架构与 **MiniCPM3-4B** 分词器进行实验验证。每个实验均在 **1000 亿 Token** 的训练量下进行，采用 **衰减验证（Decay Verification）** 方法（基于 1.3T 预训练基座模型进行退火）。我们使用 [OpenCompass](https://github.com/open-compass/opencompass) 作为评估框架。评估基准包括：
+我们使用 **衰减验证（Decay Verification）** 方法评估数据质量：在 **MiniCPM-1.2B** 基座模型（使用 **MiniCPM3-4B** 分词器，预训练 1.3T tokens）上继续训练 **~100B tokens**（30% 目标数据 + 70% 通用数据）。我们使用 [OpenCompass](https://github.com/open-compass/opencompass) 作为评估框架。评估基准包括：
 
 - **数学推理：** GSM8K、MATH500、Math-Bench、R-Bench-Math
 - **代码生成：** HumanEval、MBPP
 - **综合知识：** MMLU、MMLU-STEM
 
-### 🔧 实验设置
-
-我们使用 **衰减验证（Decay Verification）** 方法评估数据质量：在 **MiniCPM-1.2B** 基座模型（预训练 1.3T tokens）上继续训练 **~100B tokens**（30% 目标数据 + 70% 通用数据）。
-
 ### L0 解析策略有效性
 
 为公平对比不同解析策略，我们在 **2023-2024** 年分布的数据子集上进行实验。我们使用不同解析器重新解析原始 HTML，并对**所有基线应用相同的 L1 清洗算子**。该对比展示了我们 **L0 解析器 + L1 过滤管线的综合收益**。