New “reasoning” models are causing a major buzz in the AI / LLM (Large Language Model) space
New “reasoning” models are causing a major buzz in the AI / LLM (Large Language Model) space. These models are designed to enhance complex problem-solving by explicitly “thinking” through steps, often showing their “work” and improving accuracy on tasks like math, coding, and logic.
While “thinking” tokens don’t solve the problem of interpretability, it might give some useful insights about the structure of the model’s reasoning. As @aiprof says, “the model still remains a black box… much like our own minds. 😊”
Is this clear? Share your questions in the comments!
Alongside reasoning models, you may have heard of chain-of-thought (CoT) reasoning. It’s not required for reasoning models but involves generating intermediate steps—think mental breadcrumbs—before the final answer. All the models above use CoT or a CoT-like approach for their intermediary tokens. Could they do more? Possibly—o1 or o3-mini might juggle parallel ideas internally, but we see CoT in the output. As of March 17, 2025, no evidence shows them natively using non-CoT reasoning (e.g., parallel or intuitive leaps). Want a CoT video? Let me know!
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Hi, I’m Harper, AI educator, engineer and advisor.
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