Update README.md

README.md

@@ -12,41 +12,67 @@ pipeline_tag: text-generation
 ---
 <div align='center'>
 <h2>Walk Before You Run! <br/>Concise LLM Reasoning via Reinforcement Learning</h2>
-
-<!-- TODO:  Paper, Models-->
-[![Paper](https://img.shields.io/badge/paper-5f16a8?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2505.21178)
-<a href="https://huggingface.co/collections/Nickyang/conciser-6827718942b90a6390db50c1" target="_blank"><img alt="Hugging Face"
-    src="https://img.shields.io/badge/HuggingFace-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor"/></a>
 </div>
 
-
 ## 🎉News
 
 - **[2025/05/27]** 🎉 We release [**ConciseR-Zero-7B**](https://huggingface.co/Nickyang/ConciseR-Zero-7B) and [**ConciseR-Zero-7B-Preview**](https://huggingface.co/Nickyang/ConciseR-Zero-7B-Preview).
 
-## ✨Key Results
-
-We report Pass@1 accuracy averaged over 32 samples for each problem.
-
-| Model | AIME 2024 | MATH-500 | AMC 2023 | Minerva | Olympiad | Avg. Score |
-|-------|-----------|-----------|-----------|---------|----------|------------|
-| Qwen2.5-1.5B-Base | 0.0 | 3.3 | 2.5 | 1.8 | 1.5 | 1.82 |
-| Qwen2.5-1.5B-Instruct | 1.3 | 57.5 | 26.2 | 19.4 | 20.3 | 24.9 |
-| Qwen2.5-Math-1.5B-Base | 11.3 | 51.7 | 44.0 | 11.3 | 26.0 | 28.9 |
-| Qwen2.5-Math-1.5B-Instruct | 12.0 | 74.7 | 26.7 | 35.0 | 37.9 | 37.3 |
-| DeepSeek-R1-Distill-Qwen-1.5B | 28.8 | 82.8 | 62.9 | 26.5 | 43.3 | 48.9 |
-| DeepScaleR-1.5B-Preview | 43.1 | 87.8 | 73.6 | 30.2 | 50.0 | 56.9 |
-| FastCuRL-1.5B-Preview | 43.1 | 88.0 | 74.2 | 31.6 | 50.4 | 57.5 |
-| FastCuRL-1.5B-V3 | 49.6 | 90.5 | 78.5 | 34.7 | 54.5 | 61.6 |
-| | | | | | | |
-| Qwen2.5-7B-Base | 3.3 | 64.6 | 30.0 | 25.7 | 29.0 | 30.5 |
-| Qwen2.5-7B-Instruct | 12.3 | 77.1 | 52.8 | 34.9 | 38.7 | 43.2 |
-| Qwen2.5-Math-7B-Base | 20.7 | 64.3 | 56.2 | 17.3 | 29.0 | 37.5 |
-| Qwen2.5-Math-7B-Instruct | 15.7 | 82.9 | 67.0 | 35.0 | 41.3 | 48.4 |
-| Eurus-2-7B-PRIME | 17.8 | 80.1 | 63.0 | 37.5 | 43.9 | 48.5 |
-| Open-Reasoner-Zero-7B | 19.7 | 83.9 | 59.5 | 31.6 | 47.6 | 48.5 |
-| SimpleRL-Zero-7B | 14.0 | 77.9 | 58.0 | 33.0 | 39.0 | 44.4 |
-| SimpleRL-Zero-Math-7B | 22.7 | 76.9 | 62.2 | 30.1 | 39.3 | 46.2 |
-| Oat-Zero-7B | 28.0 | 79.4 | 66.2 | 34.4 | 43.8 | 50.4 |
-| ConciseR-Zero-7B-Preview (Stage-1) | 42.8 | 83.0 | 73.9 | 31.8 | 45.1 | 55.3 |
-| ConciseR-Zero-7B (Stage-2) | 43.3 | 83.0 | 76.7 | 31.5 | 46.0 | 56.1 |
+
+## Usage
+
+```python
+import vllm
+
+
+def apply_template(question: str):
+    return ("""<|startoftext|>A conversation between User and Assistant. The User asks a question, and the Assistant solves it. \
+The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. \
+The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, \
+i.e., <think> reasoning process here </think> <answer> answer here </answer>. \
+Please reason step by step, and put your final answer within \\boxed{}.
+
+User:
+{query}
+
+Assistant:
+""".replace("{query}", question))
+
+model_name = "Nickyang/ConciseR-Zero-7B"
+
+sampling_params = vllm.SamplingParams(
+    n=32,
+    temperature=0.6,
+    top_p=1.0,
+    max_tokens=3072,
+)
+
+model = vllm.LLM(
+    model_name,
+    max_model_len=4096,
+    dtype="bfloat16",
+    enable_prefix_caching=True,
+)
+
+prompts = [
+    "How many positive whole-number divisors does 196 have?"
+]
+prompts = list(map(apply_template, prompts))
+outputs = model.generate(prompts, sampling_params)
+
+print(outputs)
+```
+
+## Citation
+
+```latex
+@misc{song2025conciser,
+      title={Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning}, 
+      author={Mingyang Song and Mao Zheng},
+      year={2025},
+      eprint={2505.21178},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.21178}, 
+}
+```