The reasoning ability of a large language model is a key indicator for measuring its intelligence level. From early simple Q&A to current complex reasoning tasks, the reasoning ability of large models has undergone multiple technological leaps. In 2022, OpenAI proposed the Mind Chain Hint method, which guides the model to gradually demonstrate the reasoning process and achieves significant improvements in tasks such as mathematical problems and logical reasoning. Subsequently, researchers conducted extensive exploration on how to further enhance the model's reasoning ability.
The core idea of the thought chain is to enable the model to form intermediate reasoning steps before providing the final answer. This method has increased the accuracy of GPT-3 on the GSM8K mathematical benchmark from 18% to 58%. Furthermore, self consistency technology improves accuracy to 74% by repeatedly sampling and selecting the most consistent answer. Anthropic's Constitutional AI and Google's Self Refine enable the model to have self correcting capabilities, and the model undergoes self review and correction after generating answers.
The O1 series models released in 2025 introduce a new paradigm of inference time calculation - allowing models to engage in deeper thinking during the inference phase rather than simply generating the next word. This method significantly improves the performance of the model on hard tasks such as competitive mathematics and scientific reasoning. Open source models such as DeepSeeker R1 have also demonstrated similar capabilities. Expansion methods such as mind trees and mind maps further break through the limitations of linear thinking chains, allowing models to explore multiple reasoning paths and backtrack.
The improvement of reasoning ability also brings about an increase in reasoning costs. How to strike a balance between inference quality and computational resources is currently one of the key research directions. In the future, large models with stronger reasoning abilities will play an irreplaceable role in fields that require deep thinking, such as scientific research, code generation, and legal analysis.
【 Reference sources 】 Mind Chain paper (Wei et al., 2022), OpenAI o1 system card, DeepSeek-R1 technology report. <｜end▁of▁thinking｜>
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