Update README: align benchmark names and categories with paper

README.md

@@ -162,10 +162,10 @@ ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L3-Conversation-Synt
 
 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:** MATH500, GSM8K, Math-Bench, R-Bench-Math
-- **Code Generation:** HumanEval, MBPP
-- **English:** MMLU, ARC-E, ARC-C, BBH, CSQA, HellaSwag, OBQA, PIQA, SIQA, WinoGrande
-- **Chinese:** CMMLU, C-Eval
+- **General English:** MMLU, ARC-E, ARC-C, BigBench Hard (BBH), CommonSenseQA, HellaSwag, OpenbookQA, PIQA, SIQA, Winogrande
+- **General Chinese:** C-Eval, CMMLU
+- **Math Reasoning:** MATH500, GSM8K, Math-Bench, R-Bench-Math
+- **Code Reasoning:** MBPP, HumanEval
 
 ### Effectiveness of L0 Parsing Strategy
 

README_ZH.md

@@ -123,10 +123,10 @@ ds = load_dataset("openbmb/UltraData-Math", "UltraData-Math-L3-Conversation-Synt
 
 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：
 
+- **通用英文：** MMLU、ARC-E、ARC-C、BigBench Hard (BBH)、CommonSenseQA、HellaSwag、OpenbookQA、PIQA、SIQA、Winogrande
+- **通用中文：** C-Eval、CMMLU
 - **数学推理：** MATH500、GSM8K、Math-Bench、R-Bench-Math
-- **代码生成：** HumanEval、MBPP
-- **英文：** MMLU、ARC-E、ARC-C、BBH、CSQA、HellaSwag、OBQA、PIQA、SIQA、WinoGrande
-- **中文：** CMMLU、C-Eval
+- **代码推理：** MBPP、HumanEval
 
 ### L0 解析策略有效性