With Gemini 2.5 in AI studio you can now increase the amount of thinking tokens, and it definitely makes a difference. O3 pro is most likely O3 with an expanded thinking token budget.
> The ”reasoning” it outputs does not have a causal relationship with the end result.
It absolutely does.
Now, we can argue all about whether it's truly "reasoning", but I've certainly seen cases where if you ask it a question but say "Give just the answer", it'll consistently give a wrong answer, whereas if you let it explain its thought process before giving a final answer, it'll consistently get it right.
LLMs are at their core just next-token guessing machines. By allowing them to output extra "reasoning" tokens, it can prime the context to give better answers.
Think of it like solving an algebraic equation. Humans can't typically solve any but the most trivial equations in a single step, and neither can an LLM. But like a human, an LLM can solve one if it takes it one step at a time.
With Gemini 2.5 in AI studio you can now increase the amount of thinking tokens, and it definitely makes a difference. O3 pro is most likely O3 with an expanded thinking token budget.
Isn't that just increasing the upper bound on thinking tokens, which is rarely hit even on much lower levels?
Or my favourite, tell Claude to "ultrathink"
It is not thinking. It is trying to deceive you. The ”reasoning” it outputs does not have a causal relationship with the end result.
> The ”reasoning” it outputs does not have a causal relationship with the end result.
It absolutely does.
Now, we can argue all about whether it's truly "reasoning", but I've certainly seen cases where if you ask it a question but say "Give just the answer", it'll consistently give a wrong answer, whereas if you let it explain its thought process before giving a final answer, it'll consistently get it right.
LLMs are at their core just next-token guessing machines. By allowing them to output extra "reasoning" tokens, it can prime the context to give better answers.
Think of it like solving an algebraic equation. Humans can't typically solve any but the most trivial equations in a single step, and neither can an LLM. But like a human, an LLM can solve one if it takes it one step at a time.
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The longer "it" reasons, the more attention sinks are used to come to a "better" final output.
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