Probability(y | x)
> Prediction implies there is some "truth" or event or something that you can test against
there absolutely is a ground truth during training. the core predict-the-next-most-likely-token part of an LLM has a ground truth next-token. that's why you don't end up with generated text like: fish spurious send cattle chocolate phone happy meaning ball orange board canada.
> optimizes to predict the next token in training data
that is the optimization goal in training the next-most-likely-token core of an LLM, it basically translates to maximise the likelihood of predicting the next token x_i given the previous tokens
L(θ) = −log Π^n_{i=1} f_θ(x_i | x1, ..., x_{i−1})
(edit: sorry for the ADHD edits)
Respectfully, you are miles out of your depth. GPT-2 didn't use any reinforcement learning and is often given as a toy example. That release was 2019 and models now go through a various phases of training with different objective functions and optimizers.
> Autoregressive LLMs generate tokens one at a time, disputing this is just plain wrong.
next-token prediction i.e. the bit built during pre-training.
at no point in your reply to GP did you specify that you were referring to post-training. respectfully, it seems like this one is on you pal :shrug:
> GPT-2 didn't use any reinforcement learning and is often given as a toy example. That release was 2019 and models now go through a various phases of training with different objective functions and optimizers.
yeah. so? the toy example works for pre-training. see above.
again, the finished product wasn't what was discussed by GP, and you didn't clarify that you were switching to discussing RL (which is still probabilistic btw)
You aren't in this field. You are clearly wrong and just can't handle it.
To understand how an engine works, it's important to understand what a piston does as part of the engine.
The model weights change as the model goes through the training process. They aren't stored after pre-training is done and other weights are put somewhere else. It's more like pottery - the thing changes. It's not correct to say something is soft and malleable because it once was.
Yes. They do. You are absolutely right about that.
But the model architecture doesn't change as a result of the training process. A piston doesn't suddenly turn into a digital watch as a result of tuning an engine. Similarly, the transformer part of a GPT model doesn't suddenly turn into something else as a result of optimizing a loss function.
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i've got other stuff to do, so i'm stopping here.
An aside, I finally do appreciate single column format now, makes it easier to convert to epub.
If this base is then trained using RL towards a different objective (maths and coding), the model becomes fundamentally a different thing and the recent models are clear evidence of that, regardless of they fact they remain autoregressive.
If you modify an engine to increase it’s output by adding sensors and an ECU, you don’t change the fact that is powered by gas.
If you use RL to increase the accuracy, it’s still a next token prediction, just more accurate.