From GP, i.e. the context for this local part of the thread
> 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.
All modern LLMs that actually get used go through post-training. The finished product is something which has been through post training. So they are not next token prediction machines.
> The finished product is something which has been through post training.
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 are conflating "half built" with "a piece of a system".
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.
> The model weights change as the model goes through the training process.
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.
Just skimming through here but I think you have the wrong ideas with llms, I’d recommend Andrew Ngs course (correct me if you’ve already seen it or something similar).
I am not an expert, but I do understand the distinction that is being made here. It makes sense to describe the result of pre-training as a ‘next token’ predictor as that’s what it’s been trained to do, not because it’s an autoregressive architecture that produces tokens one at a time.
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.
So, this is the cause of the problem.... People take an intro to LLMs course, follow happily along, and don't realize there is more to it than the next token prediction. And those courses teach how LLMs were built in 2017-2020 maybe. Then RL got added to the mix. The current models really are very different to the models from then - everything that is now considered "post-training" isn't doing next token prediction.
When you are done with the section on RLVR, consider whether the model is predicting tokens, or making moves. There is a reason the word "policy" is used in RL.
You're using the fact the both parts of training affect the same weights to support your argument that they're making the system do something fundamentally different after RL?
> 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.