Update README: Simplify experimental setup, unify table column order, remove commonsense benchmarks

README.md

@@ -135,44 +135,33 @@ We used the **MiniCPM-1.2B** model architecture and **MiniCPM3-4B** tokenizer fo
 - **Mathematical Reasoning:** GSM8K (4-shot), MATH (4-shot), Math-Bench
 - **Code Generation:** HumanEval (0-shot), MBPP (3-shot)
 - **Comprehensive Knowledge:** MMLU (5-shot), MMLU-STEM (5-shot)
-- **Commonsense Reasoning:** ARC-E/C (0-shot), BBH (3-shot), CommonSenseQA (8-shot), HellaSwag (0-shot), OpenBookQA (0-shot), PIQA (0-shot), SIQA (0-shot), Winogrande (0-shot)
 
 ### 🔧 Experimental Setup
 
-Following the **Decay Verification** method described in the paper, we evaluated the data quality by continuing the pre-training of a **MiniCPM-1.2B** base model (trained on 1.3T tokens).
-
-| Hyperparameter | Value | Description |
-| :--- | :--- | :--- |
-| **Base Model** | MiniCPM-1.2B | Pre-trained on 1.3T MiniCPM-4 corpus |
-| **Training Tokens** | ~100B | 20,000 steps |
-| **Data Mixture** | 30% Target / 70% Base | 30% UltraData-Math + 70% General Data |
-| **Sequence Length** | 4096 | |
-| **Global Batch Size** | 1280 | Micro batch size 10 |
-| **Learning Rate** | 7.5e-4 $\to$ 3.75e-5 | Exponential decay |
-| **Optimizer** | AdamW | with Maximal Update Parameterization (µP) |
+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.
 
-| Parser | Average | MMLU | GSM8K | HumanEval | math | mbpp_full | mmlu-stem |
+| Parser | Average | MMLU | MMLU-STEM | Math | GSM8K | MBPP | HumanEval |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| **UltraData-Math-L0-Parser (Ours)** | **43.44** | 51.41 | 54.97 | **31.71** | **28.72** | 47.10 | 46.76 |
-| trafilatura + w3m | 42.33 | 50.95 | 54.51 | 27.44 | 27.64 | **47.93** | 45.52 |
-| trafilatura | 42.44 | 51.42 | **56.03** | 26.83 | 28.08 | 45.64 | 46.62 |
-| Megamath | 42.32 | **51.46** | 54.06 | 29.88 | 26.04 | 45.64 | **46.81** |
-| magic-html + w3m | 41.29 | 51.23 | 51.63 | 26.83 | 26.58 | 45.02 | 46.45 |
+| **UltraData-Math-L0-Parser (Ours)** | **43.44** | 51.41 | 46.76 | **28.72** | 54.97 | 47.10 | **31.71** |
+| trafilatura + w3m | 42.33 | 50.95 | 45.52 | 27.64 | 54.51 | **47.93** | 27.44 |
+| trafilatura | 42.44 | 51.42 | 46.62 | 28.08 | **56.03** | 45.64 | 26.83 |
+| Megamath | 42.32 | **51.46** | **46.81** | 26.04 | 54.06 | 45.64 | 29.88 |
+| magic-html + w3m | 41.29 | 51.23 | 46.45 | 26.58 | 51.63 | 45.02 | 26.83 |
 
 
 ### Pipeline Effectiveness (L1 vs L2 vs L3)
 
 To validate the effectiveness of our L0-L3 hierarchical 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**.
 
-| Dataset | Average | MMLU | ARC-E | ARC-C | BBH | CSQA | Hella. | OBQA | PIQA | SIQA | Wino. | Math | GSM8K | MBPP | HumanEval | CMMLU | C-Eval |
-| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **UltraData-Math-L1** | 48.39 | 51.41 | 54.50 | 37.29 | 37.75 | 60.44 | 58.02 | 41.60 | 74.21 | 41.71 | 57.14 | 27.78 | 54.66 | 44.71 | 29.88 | 51.28 | 51.89 |
-| **UltraData-Math-L2** | 48.59 | 50.93 | 55.20 | 36.95 | 39.27 | 60.20 | 57.52 | 39.80 | 74.48 | 44.73 | 57.77 | 29.20 | 52.92 | 44.50 | 32.32 | 51.13 | 50.55 |
-| **UltraData-Math-L3** | **52.23** | **51.67** | **59.79** | **38.98** | **43.62** | **61.18** | **58.27** | **57.00** | **74.76** | 43.35 | **59.04** | **37.02** | **61.79** | **49.27** | **32.93** | **52.87** | **54.08** |
+| Dataset | Average | MMLU | MMLU-STEM | Math | GSM8K | MBPP | HumanEval |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **UltraData-Math-L1** | 42.31 | 51.41 | 45.44 | 27.78 | 54.66 | 44.71 | 29.88 |
+| **UltraData-Math-L2** | 42.57 | 50.93 | 45.52 | 29.20 | 52.92 | 44.50 | 32.32 |
+| **UltraData-Math-L3** | **46.44** | **51.67** | **45.93** | **37.02** | **61.79** | **49.27** | **32.93** |
 
 *Note: Results demonstrate that higher-tier data (L3) significantly boosts mathematical reasoning (MATH, GSM8K) and general capabilities.*
 
@@ -180,13 +169,13 @@ To validate the effectiveness of our L0-L3 hierarchical framework, we conducted
 
 We used a single dataset for independent training to directly compare the effects of different data sources:
 
-| Model | Average | MMLU | GSM8K | HumanEval | math | mbpp_full | mmlu-stem | R-bench | Math-bench |
+| Model | Average | MMLU | MMLU-STEM | Math | GSM8K | MBPP | HumanEval | R-bench | Math-bench |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| **UltraData-Math (Ours)** | **43.79** | 51.67 | **61.79** | 32.93 | **37.02** | **49.27** | 45.93 | 23.38 | **48.33** |
-| Nemotron-cc 4plus mind | 43.45 | 52.09 | 59.97 | 34.76 | 35.96 | 48.03 | 45.99 | **23.51** | 47.25 |
-| Nemotron-cc 4plus | 42.62 | 51.96 | 58.45 | **35.37** | 33.40 | 46.47 | 45.67 | 22.74 | 46.92 |
-| MegaMath-Web-Pro | 41.38 | **53.16** | 56.71 | 31.71 | 32.12 | 47.10 | **47.15** | 21.23 | 41.83 |
-| FineMath-4+ | 40.51 | 50.90 | 56.25 | 29.88 | 29.84 | 48.96 | 44.98 | 18.93 | 44.33 |
+| **UltraData-Math (Ours)** | **43.79** | 51.67 | 45.93 | **37.02** | **61.79** | **49.27** | 32.93 | 23.38 | **48.33** |
+| Nemotron-cc 4plus mind | 43.45 | 52.09 | 45.99 | 35.96 | 59.97 | 48.03 | 34.76 | **23.51** | 47.25 |
+| Nemotron-cc 4plus | 42.62 | 51.96 | 45.67 | 33.40 | 58.45 | 46.47 | **35.37** | 22.74 | 46.92 |
+| MegaMath-Web-Pro | 41.38 | **53.16** | **47.15** | 32.12 | 56.71 | 47.10 | 31.71 | 21.23 | 41.83 |
+| FineMath-4+ | 40.51 | 50.90 | 44.98 | 29.84 | 56.25 | 48.96 | 29.88 | 18.93 | 44.33 |
 
 ## ❤️ Acknowledgements