theshyustc/CoRT-Hint-Engineering-1.5B-RL
Theshyustc/CoRT-Hint-Engineering-1.5B-RL is a 1.5 billion parameter model developed by theshyustc, based on DeepSeek-R1-Distill-Qwen-1.5B. It is trained with the CoRT (Code-integrated Reasoning within Thinking) framework, specializing in mathematical reasoning by integrating natural language with Python code execution. This model uniquely employs a Hint-Engineering approach for superior token efficiency, using 50% fewer tokens than baselines while maintaining competitive performance. It excels at complex mathematical problem-solving through strategic hint placement and multi-turn tool-integrated reasoning.
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CoRT-Hint-Engineering-1.5B-RL: Mathematical Reasoning with Enhanced Efficiency
CoRT-Hint-Engineering-1.5B-RL is a 1.5 billion parameter model developed by theshyustc, built upon the DeepSeek-R1-Distill-Qwen-1.5B base. It leverages the CoRT (Code-integrated Reasoning within Thinking) framework, which integrates natural language reasoning with Python code execution to tackle complex mathematical problems.
Key Capabilities & Differentiators
- Superior Token Efficiency: Achieves competitive performance using 50% fewer tokens compared to baseline models, making it highly efficient.
- Hint-Engineering: Employs a novel Hint-Engineering approach that strategically inserts targeted hints at critical decision points during reasoning. This optimizes code usage, balances calculation and verification (51.1% vs 48.9%), and minimizes unnecessary steps.
- Multi-turn Tool-Integrated Reasoning: Supports interactive code execution within reasoning chains, allowing for dynamic problem-solving.
- Strong Mathematical Performance: Demonstrates solid accuracy across various mathematical benchmarks, including AIME24 (41.0%), AMC23 (70.0%), MATH500 (85.8%), and Olympiad (55.6%), with an average accuracy of 56.4%.
When to Use This Model
This model is particularly well-suited for:
- Mathematical Problem Solving: Excels in tasks requiring complex mathematical reasoning and precise calculations.
- Resource-Constrained Environments: Its token efficiency makes it a strong candidate for applications where computational resources or inference costs are a concern.
- Interactive Reasoning Systems: Designed for multi-turn, tool-integrated reasoning, making it ideal for applications that benefit from dynamic code execution and verification.
Important Note: Optimal performance requires using the specialized inference script from the CoRT GitHub repository to enable its multi-turn tool-integrated reasoning capabilities.