In this episode, we discuss Thinking LLMs: General Instruction Following with Thought Generation by Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar. The paper introduces a novel approach to enhance Large Language Models by incorporating an iterative thought process before response generation, which helps in overcoming limitations of current models that lack explicit thinking. This process involves learning through an exploration and optimization framework without needing direct human supervision of thought processes. By employing a judge model for evaluation and preference optimization, the method shows improved performance in reasoning, planning, and other domains such as marketing and health.
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