I think the most useful word in both cases is "extrapolating".
An LLM extrapolates from its context window to the immediate next token. This word applies whether you view what's happening as "reasoning", "prediction", or as a math function.
If you're assigning steering 70 of your 100 output points because it's what you think we should go with most of the time in this situation, I'm going to call that a prediction of how to steer.
The point was, if your internal model of the world makes a prediction of a negative outcome at some point in the future, and you optimise your individual actions to avoid that negative outcome, then wouldn’t it make sense to focus on the fact you’re building and optimizing towards an internal world model rather than the fact you’re executing your actions one at a time in series?
I think the most useful word in both cases is "extrapolating".
An LLM extrapolates from its context window to the immediate next token. This word applies whether you view what's happening as "reasoning", "prediction", or as a math function.
No, those aren't synonyms at all.
If I steer a car to avoid a predicted collision with a wall, this is not me 'predicting' the car. I am steering the car based on a prediction.
If you're assigning steering 70 of your 100 output points because it's what you think we should go with most of the time in this situation, I'm going to call that a prediction of how to steer.
The point was, if your internal model of the world makes a prediction of a negative outcome at some point in the future, and you optimise your individual actions to avoid that negative outcome, then wouldn’t it make sense to focus on the fact you’re building and optimizing towards an internal world model rather than the fact you’re executing your actions one at a time in series?
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