Update README.md

README.md

@@ -119,23 +119,28 @@ DeepMath-1.5B is created by finetuning deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
 
 <sub>Difficulty distribution comparison.</sub> </div>
 
-**2. Broad Topical Diversity**: The dataset spans a wide spectrum of mathematical subjects, including Algebra, Calculus, Number Theory, Geometry, Probability, and Discrete Mathematics.
+**2. Data Diversity and Novelty**: DeepMath-103K spans a wide spectrum of mathematical subjects, including Algebra, Calculus, Number Theory, Geometry, Probability, and Discrete Mathematics.
 
 <div align="center"> <img src="./assets/github-domain.png" width="50%"/>
 
 <sub>Hierarchical breakdown of mathematical topics covered in DeepMath-103K.</sub></div>
 
-**4. Rigorous Decontamination**: Built from diverse sources, the dataset underwent meticulous decontamination against common benchmarks using semantic matching. This minimizes test set leakage and promotes fair model evaluation.
+The problems in DeepMath-103K are novel and unique, whereas many existing datasets are similar and overlap.
+<div align="center"> <img src="./assets/github-tsne.png" width="70%"/>
+
+<sub>Embedding distributions of different datasets.</sub></div>
+
+**3. Rigorous Decontamination**: Built from diverse sources, DeepMath-103K underwent meticulous decontamination against common benchmarks using semantic matching. This minimizes test set leakage and promotes fair model evaluation.
 
 <div align="center"> <img src="./assets/github-contamination-case.png" width="80%"/>
 
 <sub>Detected contamination examples. Subtle conceptual overlaps can also be identified.</sub> </div>
 
-**5. Rich Data Format**: Each sample in `DeepMath-103K` is structured with rich information to support various research applications:
+**4. Rich Data Format**: Each sample in DeepMath-103K is structured with rich information to support various research applications:
 
 <div align="center"> <img src="./assets/github-data-sample.png" width="90%"/>
 
-<sub>A data sample from DeepMath-103K.</sub> </div>
+<sub>An example data sample from DeepMath-103K.</sub> </div>
 
 - **Question**: The mathematical problem statement.
 - **Final Answer**: A reliably verifiable final answer, enabling robust rule-based reward functions for RL.
@@ -145,22 +150,73 @@ DeepMath-1.5B is created by finetuning deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
 
 ## 📊Main Results
 
-`DeepMath-Zero-7B` and `DeepMath-1.5B` are trained on the `DeepMath-103K` dataset via RL. These models are initialized from `Qwen2.5-7B-Base` and `R1-Distill-Qwen-1.5B`, respectively.
+DeepMath serise models achieve many **SOTA** results on challenging math benchmarks:
+
+<div align="center"> <img src="./assets/github-main.png" width="90%"/>
+
+<sub>Math reasoning performance.</sub> </div>
+
+
+## 🎯Quick Start
+
+#### Environment Preparation
+
+```shell
+git clone --recurse-submodules https://github.com/zwhe99/DeepMath.git && cd DeepMath
+
+conda create -y -n deepmath python=3.12.2 && conda activate deepmath
+pip3 install ray[default]
+pip3 install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
+pip3 install flash-attn==2.7.4.post1 --no-build-isolation
+pip3 install omegaconf==2.4.0.dev3 hydra-core==1.4.0.dev1 antlr4-python3-runtime==4.11.0 vllm==0.7.3
+pip3 install math-verify[antlr4_11_0]==0.7.0 fire deepspeed tensorboardX prettytable datasets transformers==4.49.0
+pip3 install -e verl
+```
+
+
 
+#### Evaluation
+
+```shell
+VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 VLLM_ATTENTION_BACKEND=XFORMERS VLLM_USE_V1=1 VLLM_WORKER_MULTIPROC_METHOD=spawn python3 uni_eval.py \
+    --base_model zwhe99/DeepMath-Zero-7B \
+    --chat_template_name orz \
+    --system_prompt_name simplerl \
+    --output_dir  \
+    --bf16 True \
+    --tensor_parallel_size 8 \
+    --data_id zwhe99/MATH \
+    --split math500 \
+    --max_model_len 32768 \
+    --temperature 0.6 \
+    --top_p 0.95 \
+    --n 16
+```
+
+
+
+#### Training
+
+* Data Preparation
+
+  ```shell
+  DATA_DIR=/path/to/your/data
+  python3 verl/examples/data_preprocess/deepmath_103k.py --local_dir $DATA_DIR
+  ```
+
+* Start Ray
+
+  ```shell
+  # Head node (×1)
+  ray start  --head --port=6379  --node-ip-address=$HEAD_ADDR --num-gpus=8
+  
+  # Worker nodes (×7 or ×11)
+  ray start  --address=$HEAD_ADDR:6379 --node-ip-address=$WORKER_ADDR --num-gpus=8
+  ```
+
+* Launch training at head node. See `scripts/train` for training scripts.
 
-|          Model           | MATH 500 |  AMC23   | Olympiad Bench | Minerva Math |  AIME24  |  AIME25  |
-| :----------------------: | :------: | :------: | :------------: | :----------: | :------: | :------: |
-|     Qwen2.5-7B-Base      |   54.8   |   35.3   |      27.8      |     16.2     |   7.7    |   5.4    |
-|  Open-Reasoner-Zero-7B   |   81.8   |   58.9   |      47.9      |     38.4     |   15.6   |   14.4   |
-| Qwen-2.5-7B-SimpleRL-Zoo |   77.0   |   55.8   |      41.0      |     41.2     |   15.6   |   8.7    |
-|     [DeepMath-Zero-7B](https://huggingface.co/zwhe99/DeepMath-Zero-7B)     | **85.5** | **64.7** |    **51.0**    |   **45.3**   | **20.4** | **17.5** |
 
-|          Model          | MATH 500 |  AMC23   | Olympiad Bench | Minerva Math |  AIME24  |  AIME25  |
-| :---------------------: | :------: | :------: | :------------: | :----------: | :------: | :------: |
-|  R1-Distill-Qwen-1.5B   |   84.7   |   72.0   |      53.1      |     36.6     |   29.4   |   24.8   |
-| DeepScaleR-1.5B-Preview |   89.4   |   80.3   |      60.9      |     42.2     | **42.3** |   29.6   |
-|  Still-3-1.5B-Preview   |   86.6   |   75.8   |      55.7      |     38.7     |   30.8   |   24.6   |
-|   [DeepMath-1.5B](https://huggingface.co/zwhe99/DeepMath-1.5B)         | **89.9** | **82.3** |    **61.8**    |   **42.5**   |   37.3   | **30.8** |
 
 ## 🙏 Acknowledgements
 
@@ -171,6 +227,8 @@ This work can not be done without the help of the following works:
 - **[TIGER-Lab/WebInstructSub](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)**: Instruction data from MathStackExchange and ScienceStackExchange.
 - **[AI-MO/NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT)**: Approximately 860k math problems.
 
+
+
 ## 📚 Citation
 ```bibtex
 @article{deepmath,
@@ -182,4 +240,4 @@ This work can not be done without the help of the following works:
   primaryClass={cs.CL},
   url={https://arxiv.org/abs/2504.11456}, 
 }
-```
+```