Researchers from ByteDance unveiled the Reinforced Fine-Tuning (ReFT) method to enhance the reasoning skills of LLMs, using math problem-solving as an example. By combining supervised fine-tuning and reinforcement learning, ReFT optimizes learning by exploring multiple reasoning paths, outperforming traditional methods and improving generalization in extensive experiments across different datasets. For more details, refer to the paper.
ByteDance AI Research Unveils Reinforced Fine-Tuning (ReFT) Method to Enhance Learning LLMs for Reasoning
Improving Reasoning Skills
One practical method to enhance the reasoning skills of middle managers is Reinforced Fine-Tuning (ReFT). This approach helps the algorithm learn from multiple annotated reasoning paths associated with a given question, enhancing its overall performance and adaptability.
ReFT Method
ReFT combines supervised fine-tuning with online reinforcement learning using the Proximal Policy Optimization (PPO) algorithm. This method significantly outperforms traditional supervised fine-tuning in math problem-solving, leading to better reasoning capability and generalizability for middle managers.
Value and Practical Solutions
ReFT’s effectiveness and practical value have been demonstrated through extensive experiments, surpassing traditional methods in performance and generalization. It also exhibits compatibility with inference-time strategies and shows significant improvements over natural language prompts.
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- ByteDance AI Research Unveils Reinforced Fine-Tuning (ReFT) Method to Enhance the Generalizability of Learning LLMs for Reasoning with Math Problem Solving as an Example
- MarkTechPost
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