Geek Out Time: Tree-of-Thought for LLM Reasoning

As LLMs get better at producing structured explanations, it has become increasingly common to describe them as “reasoning models.” The release of OpenAI’s o-series, particularly o1, along with DeepSeek R1 etc, made this framing much more explicit. Unlike earlier models that relied primarily on scaling pre-training data and parameters, the o-series introduced reasoning as a distinct axis of improvement-allocating more compute at inference time to explore intermediate steps before producing a final answer.

With chain-of-thought prompting, models can articulate intermediate reasoning, reflect on assumptions, and arrive at answers that appear logically grounded. But after o1, the idea of a reasoning model became more than just better formatting of explanations. It suggested a different scaling strategy altogether — not just bigger models, but models that deliberately spend more time “thinking.”

That raises an interesting technical question. Under the hood, what does this reasoning actually look like? Is it a single coherent internal process, or is it closer to structured search over multiple candidate paths? And if it is search, how can we make that mechanism visible?

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