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:
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.
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.
