Reasoning is one of the most interesting areas of research in the world of foundation models and one that has been accelerated since the release of GPT-o1. The more specific trend is to develop foundation models that can 'reason' before producing an output. This concept draws inspiration from how humans tackle complex problems, taking time to ponder and strategize before arriving at an answer. Research in the area of planning and reasoning is being tracked very closely as it can represent the next breakthrough in generative AI. This is the area of a recent research paper from Meta AI which explores a novel technique called Thought Preference Optimization (TPO). This is one of the most interesting papers in reasoning I've recently and, today, I would like to unpack the core ideas, examine experimental results, and consider the potential impact of this approach on the future of generative AI.
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