Reinforcement fine-tuning is a cutting-edge method to enhance AI's customization capabilities. It improves reasoning, enabling AI to solve complex problems. With applications in medicine, law, and finance, this method offers precise and efficient solutions. The three steps of reinforcement fine-tuning — data preparation, scoring, and reinforcement learning — come with clear advantages and challenges.
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