Optimization Research on Reasoning Capabilities of Large Models Based on Chain of Thought

The aim of this study is to explore a chain-of-thought-based approach to improving the reasoning ability of large language models, using the LLaMA3-8B model as a base and combining LoRA fine-tuning techniques to optimize it. The LLaMA3 model shows strong basic ability in natural language processing tasks, but when dealing with complex reasoning tasks, the model's inference steps tend not to be clear enough, this paper introduces the chain-of-thought approach, which significantly improves the model's performance in multi-step reasoning tasks by gradually unfolding the reasoning process. At the same time, this study adopts the LoRA fine-tuning technique to ensure the computational efficiency of the model, while further improving its adaptability and accuracy on specific tasks. Through experimental evaluations on several standard datasets, the results show that the LLaMA3 model combining LoRA fine-tuning and CoT methods significantly improves both reasoning accuracy and efficiency, providing new ideas and practical basis for efficient reasoning and fine-tuning of large language models.