Long CoT reasoning improves large language models’ performance on complex tasks but comes with drawbacks. The typical “think-then-answer” method slows down response times, disrupting real-time interactions like those in chatbots. It also risks inaccuracies, as errors in earlier reasoning steps can lead to a misleading final answer. Unlike humans, who often share partial thoughts or ideas before reaching a conclusion, traditional language models lack the ability to provide intermediate answers during the reasoning process. To address this issue, Apple and Duke researchers have introduced a reinforcement learning approach that enables Large Language Models (LLMs) to provide intermediate answers. This approach aims to enhance both the speed and accuracy of LLMs by allowing them to share partial answers during complex reasoning tasks. By incorporating reinforcement learning techniques, the researchers have successfully trained LLMs to provide intermediate answers that can improve overall performance. The ability to offer intermediate answers not only speeds up the response time of LLMs but also reduces the risk of errors in the final output. This advancement is particularly beneficial for applications that require real-time interactions, such as chatbots, where quick and accurate responses are essential for a seamless user experience.