The InternLM research team is dedicated to improving and developing large language models (LLMs) specifically tailored for mathematical reasoning and problem-solving. They aim to strengthen artificial intelligence’s performance ability when dealing with mathematically complex tasks, such as formal proofs and informal problem-solving.
Researchers from several esteemed institutions have worked together on producing the InternLM2-Math-Plus model series. Each model is designed to improve and enhance informal and formal mathematical reasoning, thereby bridging the gap in performance and efficiency experienced by previous models when tackling complex mathematical problems.
The series includes four different variants, each with a specific number of parameters: the 1.8B model, balancing performance and efficiency; the 7B model, which improves on current open-source models’ capabilities; the 20B model, designed for highly demanding mathematical computations; and the Mixtral8x22B model, which delivers unmatched precision and accuracy for the most complex tasks.
Several advanced techniques are incorporated in these models, including chain-of-thought reasoning, reward modeling, and a code interpreter. They are pre-trained on diverse and high-quality mathematical data, which encompasses synthetic data for numerical operations and domain-specific datasets. Further fine-tuning enhances the models’ problem-solving capabilities and their ability to verify results.
Regarding performance, the InternLM2-Math-Plus models signify a significant improvement over previous models. The 1.8B, 7B, and 20B models all outperform other models in their size categories, while the Mixtral8x22B model achieves top scores on MATH and GSM8K, demonstrating its superior problem-solving capability.
Each variant is specifically designed to address unique needs in the realm of mathematical reasoning. The 1.8B model offers a balance between performance and efficiency for applications requiring sturdy but compact models. While the 7B model is enhanced for more complex problem-solving, the 20B variant pushes the boundaries in performance, suitable for highly demanding mathematical computations. The largest model, Mixtral8x22B, delivers unparalleled accuracy, precision, and it is preferred for the most challenging tasks.
In conclusion, the development of InternLM2-Math-Plus models represents a substantial advancement in the mathematical reasoning capabilities of LLMs. By integrating advanced training techniques and utilizing extensive data sets, these models significantly enhance performance on various mathematical benchmarks. Therefore, this development signifies a significant step towards improving the functional capabilities of AI in mathematical reasoning and problem-solving.
