This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
Compass-Thinker-7B is a 7 billion-parameter language model that enhances mathematical reasoning capabilities through reinforcement learning. To address the high cost and resource constraints associated with applying reinforcement learning to existing large-scale language models, it was trained using an efficient reinforcement learning pipeline and a dataset of 30,000 verifiable mathematical problems. Step-by-step difficulty adjustments gradually unlock the model's potential and improve training efficiency. Notably, it achieves 40% accuracy in the AIME2024 evaluation, demonstrating superior mathematical reasoning performance compared to other reinforcement learning models of the same scale.
Takeaways, Limitations
•
Takeaways:
◦
We demonstrate that efficient reinforcement learning can achieve superior inference capabilities even on relatively small-scale models, rather than large-scale models.
◦
We suggest that a reinforcement learning strategy using step-by-step difficulty adjustment can effectively bring out the model's potential.
◦
It suggests that the development of high-performance inference models is possible even with limited resources, and suggests directions for future research on reinforcement learning for large-scale models.
•
Limitations:
◦
The performance evaluation of the Compass-Thinker-7B model was primarily limited to mathematical problems. Further research is needed to evaluate its performance on other types of reasoning problems.
◦
The size of the dataset used (30,000 data points) is relatively small compared to datasets used for large-scale model training. Research utilizing larger datasets may be necessary.
◦
Further research is needed to determine the generalizability of the proposed reinforcement learning pipeline. Its applicability to other types of problems and models needs to be verified.
