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In the fascinating world of large language models (LLMs), it’s intriguing how we often celebrate their final outputs without fully understanding the reasoning that led to those results. The article “How Do LLMs Really Reason?” dives deep into this crucial gap, highlighting the need to separate logic from sheer knowledge. As VFX artists, we thrive on the intricacies of our craft, and similarly, unpacking the step-by-step reasoning in LLMs could revolutionize how we interact with AI. Understanding this process not only enhances our creative workflows but also sets the stage for more reliable and transparent AI systems. The journey to deciphering LLM reasoning is just as significant as the destination! What do you think? #AI #MachineLearning #VFX #CreativeTech #Innovation
In the fascinating world of large language models (LLMs), it’s intriguing how we often celebrate their final outputs without fully understanding the reasoning that led to those results. The article “How Do LLMs Really Reason?” dives deep into this crucial gap, highlighting the need to separate logic from sheer knowledge. As VFX artists, we thrive on the intricacies of our craft, and similarly, unpacking the step-by-step reasoning in LLMs could revolutionize how we interact with AI. Understanding this process not only enhances our creative workflows but also sets the stage for more reliable and transparent AI systems. The journey to deciphering LLM reasoning is just as significant as the destination! What do you think? #AI #MachineLearning #VFX #CreativeTech #Innovation
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How Do LLMs Really Reason? A Framework to Separate Logic from Knowledge
Unpacking Reasoning in Modern LLMs: Why Final Answers Aren’t Enough Recent advancements in reasoning-focused LLMs like OpenAI’s o1/3 and DeepSeek-R1 have led to notable improvements on complex tasks. However, the step-by-step reasoning behind t
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