The InternLM2-Math-Plus: Advancing Mathematical Reasoning with Enhanced LLMs
Introduction
The InternLM research team focuses on developing large language models (LLMs) tailored for mathematical reasoning and problem-solving. These models aim to enhance artificial intelligence’s capabilities in handling complex mathematical tasks, including formal proofs and informal problem-solving.
Practical Solutions and Value
The InternLM2-Math-Plus series, comprising variants with 1.8B, 7B, 20B, and 8x22B parameters, is designed to bridge the gap in performance and efficiency in solving complex mathematical tasks. These models incorporate advanced techniques such as chain-of-thought reasoning, reward modeling, and a code interpreter, and are pre-trained on diverse, high-quality mathematical data, including synthetic data for numerical operations and domain-specific datasets.
Each variant of InternLM2-Math-Plus is tailored to address specific needs in mathematical reasoning. The 1.8B model balances performance and efficiency, the 7B model provides enhanced capabilities for more complex problem-solving tasks, the 20B model pushes the boundaries of performance, and the Mixtral8x22B model delivers unparalleled accuracy and precision for the most challenging mathematical tasks.
These models show significant improvement over existing models, with the largest model, Mixtral8x22B, achieving top scores on various benchmarks, indicating superior problem-solving capabilities.
Conclusion
The research on InternLM2-Math-Plus signifies a substantial advancement in the mathematical reasoning capabilities of LLMs. The models effectively address key challenges by integrating sophisticated training techniques and leveraging extensive datasets, enhancing performance on various mathematical benchmarks.
Sources
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