Comment by doc_ick
2 days ago
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).
2 days ago
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.
Does the architecture change or the weight change?
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.
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.
Please feel free to cite sources then, otherwise I see no relevancy from you.
Nathan Lambert wrote a good book recently, and he and his team wrote the paper below about Tulu 3 (Allen Institute). Both are good reads.
https://arxiv.org/pdf/2411.15124
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