Merge L1-L3 result note into description paragraph

README.md

@@ -178,14 +178,12 @@ To fairly compare different parsing strategies, we conducted experiments on a da
 
 ### Pipeline Effectiveness (L1 vs L2 vs L3)
 
-To validate the effectiveness of our L0-L3 tiered framework, we conducted ablation studies comparing models trained on different tiers of UltraData-Math. Unlike the L0 parser comparison above (which used a 2023-2024 subset), these results are based on the **full dataset**.
+To validate the effectiveness of our L0-L3 tiered framework, we conducted ablation studies comparing models trained on different tiers of UltraData-Math. Unlike the L0 parser comparison above (which used a 2023-2024 subset), these results are based on the **full dataset**. Results demonstrate that higher-tier data (L3) significantly boosts mathematical reasoning (MATH500, GSM8K) and general capabilities.
 
 <div align="center">
   <img src="assets/ultradata-math-l1l2l3-comparison.png" width="700"/>
 </div>
 
-*Note: Results demonstrate that higher-tier data (L3) significantly boosts mathematical reasoning (MATH500, GSM8K) and general capabilities.*
-
 ### Full Evaluation Results
 
 To compare against existing public mathematical pre-training datasets, we trained models independently on each dataset using the same model architecture and training budget (~100B tokens). The baselines include [Nemotron-CC-Math](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1), [MegaMath-Web-Pro](https://huggingface.co/datasets/LLM360/MegaMath), and [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath). All models are evaluated under identical conditions for a fair comparison:

README_ZH.md

@@ -139,14 +139,12 @@ ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L3-Conversation-Synt
 
 ### 流水线有效性（L1 vs L2 vs L3）
 
-为验证 L0-L3 分级框架的有效性，我们对使用不同层级 UltraData-Math 训练的模型进行了消融实验。与上文 L0 解析器对比（使用 2023-2024 子集）不同，以下结果基于**全量数据集**。
+为验证 L0-L3 分级框架的有效性，我们对使用不同层级 UltraData-Math 训练的模型进行了消融实验。与上文 L0 解析器对比（使用 2023-2024 子集）不同，以下结果基于**全量数据集**。结果表明，更高层级的数据（L3）显著提升了数学推理能力（MATH500、GSM8K）及通用能力。
 
 <div align="center">
   <img src="assets/ultradata-math-l1l2l3-comparison.png" width="700"/>
 </div>
 
-*注：结果表明，更高层级的数据（L3）显著提升了数学推理能力（MATH500、GSM8K）及通用能力。*
-
 ### 完整评测结果
 
 为与现有公开数学预训练数据集进行对比，我们使用相同的模型架构和训练预算（~100B tokens）在每个数据集上独立训练模型。基线包括 [Nemotron-CC-Math](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1)、[MegaMath-Web-Pro](https://huggingface.co/datasets/LLM360/MegaMath) 和 [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath)。所有模型在相同条件下评估以确保公平对比：