初始化项目，由ModelHub XC社区提供模型

Model: knoveleng/Open-RS3
Source: Original Platform

README.md

@@ -0,0 +1,48 @@
+---
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+datasets:
+- knoveleng/open-rs
+- knoveleng/open-s1
+- knoveleng/open-deepscaler
+license: mit
+pipeline_tag: text-generation
+inference: true
+library_name: transformers
+---
+
+# Model Summary
+
+This model enhances the reasoning capabilities of the small 1.5B parameter `DeepSeek-R1-Distill-Qwen-1.5B` LLM using reinforcement learning (RL). Trained efficiently on 4 A40 GPUs in under 24 hours, it achieves significant gains in mathematical reasoning benchmarks (e.g., 80% accuracy on AMC23, 46.7% on AIME24, surpassing `o1-preview`).  This cost-effective approach demonstrates the potential of RL for boosting reasoning in resource-constrained settings.
+
+
+## Evaluation
+### Performance Highlights
+- **Open-RS1**: 53.0% avg. score
+- **Open-RS2**: 55.7% avg. score, 80.0% on AMC23
+- **Open-RS3**: 56.3% avg. score, 46.7% on AIME24 (outperforms `o1-preview` at 44.6%)
+- Competitive MATH-500 scores; Minerva lags behind 7B models.
+
+![Performance Metrics](assets/performances.png)
+
+### Cost Efficiency
+Our approach uses 7,000 samples (42,000 total outputs) and costs ~$42 on 4x A40 GPUs in 24 hours, compared to thousands of dollars for baseline models.
+
+![7B Model Costs](assets/costs-7b.png)  
+![1.5B Model Costs](assets/costs-1.5b.png)
+
+
+## Citation
+If this project aids your work, please cite it as:
+```
+@inproceedings{
+    dang2026reinforcement,
+    title={Reinforcement Learning for Reasoning in Small {LLM}s: What Works and What Doesn{\textquoteright}t},
+    author={Quy-Anh Dang and Chris Ngo},
+    booktitle={Logical and Symbolic Reasoning in Language Models @ AAAI 2026},
+    year={2026},
+    url={https://openreview.net/forum?id=3pWL6Zxc4A}
+}
+```
+
+For more details, including usage instructions and further evaluation results, please refer to our [GitHub repository](https://github.com/knoveleng/open-rs).