OpenO1-SFT Supervised Fine-tuning Dataset

The OpenO1-SFT dataset is a dataset that focuses on activating the Chain-of-Thought ability of language models using the supervised fine-tuning (SFT) method, aiming to enhance the model's ability to generate coherent logical reasoning sequences. It contains 77,685 records that cover not only Chinese but also English, making the dataset useful in multilingual environments.

Each record in the dataset uses <Thought> and <Output> Labels are used to distinguish the model’s thinking process from the final answer. This structure not only ensures the consistency of the data format, but also ensures the logic, allowing the model to better learn and simulate the human thinking process.

When fine-tuning a model using the OpenO1-SFT dataset, researchers need to ensure that the model can correctly interpret <Thought> and <Output> Labels are crucial for the model to correctly identify and learn the reasoning process and answers. The model fine-tuned in this way shows significant performance improvements on multiple benchmarks, especially in tasks that require detailed reasoning steps.

The OpenO1-SFT dataset has a wide range of application scenarios, especially in areas that require a high degree of logic and reasoning ability, such as intelligent question-answering systems, educational assistance tools, and legal consulting systems. Models trained using this dataset can more accurately understand and answer complex questions and provide more detailed and reliable solutions.

In the latest research direction in the field of natural language processing, the OpenO1-SFT dataset is used to explore how to further improve the reasoning ability of language models through chain thinking activation. The goal is to enable the model to produce detailed and structured reasoning steps, so as to perform better in complex reasoning tasks. These studies not only promote the performance improvement of the model in mathematical and logical reasoning tasks, but also provide new ideas for solving more complex natural language understanding problems.