| | --- |
| | license: mit |
| | base_model: |
| | - Qwen/Qwen2.5-7B-Instruct-1M |
| | --- |
| | |
| | ### Model Card: Graph-R1 Series |
| |
|
| | 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. |
| |
|
| | #### **Model Details** |
| |
|
| | * **Model Developer**: HKUST-DSAIL |
| | * **Model Series**: Graph-R1 |
| | * **Model Variants**: |
| | * **Graph-R1-7B**: Fine-tuned from Qwen2.5-7B-Instruct-1M. |
| | * **Graph-R1-1.5B**: Fine-tuned from Qwen2.5-1.5B. |
| | * **Ablation Models**: Multiple variants based on different training configurations (e.g., data volume, training stages, reward functions, curriculum learning strategies). |
| | * **Model Type**: Small reasoning language model, specialized in solving complex NP graph-theoretic problems. |
| | * **Architecture**: |
| | * **Base Model**: Qwen2.5 |
| | * **Training Framework**: |
| | 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. |
| | 2. **Reasoning Optimization via Reinforcement Learning (RL)**: Employs a Group Relative Policy Optimization (GRPO)-based RL framework, combined with a curriculum learning strategy. |
| | * **Model Date**: 2025/04 |
| |
|
| | #### **Intended Use** |
| |
|
| | * **Primary Use Cases**: |
| | * 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). |
| | * Serving as a compact, resource-efficient reasoning model for academic research and practical applications. |
| | * **Potential Cross-Domain Applications**: |
| | * The model demonstrates transferability to other complex reasoning tasks, including mathematics, programming, STEM, and logical reasoning. |
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