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license: mit |
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base_model: |
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- Qwen/Qwen2.5-1.5B |
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### Model Card: Graph-R1 Series |
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This model card covers the Graph-R1 series of models, including the final released versions and variants used in ablation studies. All information is based on the provided research paper. |
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#### **Model Details** |
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* **Model Developer**: HKUST-DSAIL |
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* **Model Series**: Graph-R1 |
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* **Model Variants**: |
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* **Graph-R1-7B**: Fine-tuned from Qwen2.5-7B-Instruct-1M. |
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* **Graph-R1-1.5B**: Fine-tuned from Qwen2.5-1.5B. |
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* **Ablation Models**: Multiple variants based on different training configurations (e.g., data volume, training stages, reward functions, curriculum learning strategies). |
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* **Model Type**: Small reasoning language model, specialized in solving complex NP graph-theoretic problems. |
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* **Architecture**: |
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* **Base Model**: Qwen2.5 |
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* **Training Framework**: |
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1. **Cold-start Supervised Fine-Tuning (SFT)**: Fine-tuned using long Chain-of-Thought (Long-CoT) data extracted from the QwQ-32B model to inject graph reasoning knowledge. |
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2. **Reasoning Optimization via Reinforcement Learning (RL)**: Employs a Group Relative Policy Optimization (GRPO)-based RL framework, combined with a curriculum learning strategy. |
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* **Model Date**: 2025/04 |
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#### **Intended Use** |
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* **Primary Use Cases**: |
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* Solving complex graph-theoretic computational problems at the NP-Complete level, such as the Traveling Salesman Problem (TSP), Graph Edit Distance (GED), and Maximum Clique Problem (MCP). |
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* Serving as a compact, resource-efficient reasoning model for academic research and practical applications. |
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* **Potential Cross-Domain Applications**: |
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* The model demonstrates transferability to other complex reasoning tasks, including mathematics, programming, STEM, and logical reasoning. |
