HKUST-DSAIL/Graph-R1-1.5B

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:
• —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.