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Comment by danielmarkbruce | Hacker News Reader

Nope. Post training means even the raw model isn't predicting.

In what sense is the model not predicting?

There isn't a truth to test against. If I predict the next word in a sequence is "sat", we can check against the sequence. If I predict the roll of a die will be 4, we can check against it. Whether i give 100% or give a probabilistic prediction, we can check against the truth.

If I choose a specific move in chess, it's a choice. It's not a prediction. I might get a score 40 moves later given my choice, but I'm not predicting the next move.

To compare - during pre-training, the model literally tries to predict the next token (probabilistically), the training loop checks against the "right" answer, and the weights are updated based on that check. It's optimized to predict the next token.

Oh, that's somewhat philosophical. The prediction here is a mechanical thing, just random sampling along a pre-calibrated mapping of options given the tokens and their importance weights. That sampling can be called prediction if you use it that way, or it can be called thinking if you are into the psychosis too deeply. Or it can be called a CLI tool command if you're in an agentic framework

No, it's not philsophical. Because if you optimize to predict, you are doing something different to optimizing for a reward. It's a different process - different objective function, different optimization, different set up.

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