Comment by skywhopper | Hacker News Reader

Comment by skywhopper | Hacker News Reader

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Lots of red flags here. Besides synthetic training data (huge red flag), this new model is going to somehow generate “correct” (red flag) training data. Also, this new model will be much more expensive to run (red flag), so they are distilling it (back down to lower quality — red flag) and need to figure out how to make it work in ChatGPT (not ready for release — red flag), and the true impact of it anyway is for the next generation (huge red flag) model.

We’re in a constant cycle of “sure the current stuff doesn’t live up to the hype but the next release that’s just on the horizon, that one will blow you away!”

This leak is clearly targeted at creating the buzz necessary to raise the money to keep the pipe dream flowing for the time being. But just thinking through the steps outlined here, it should be clear none of this makes sense. Any “correct” synthetic training data is either going to be badly biased, or be limited to such banal “logical” output that the model it trains will be entirely unable to process natural language input and you’ll need to specify your questions in something more precise. At which point, we’re back to traditional programming languages, only with several layers of unnecessary and expensive processing going on in between.

None of those things are red flags?

> Any “correct” synthetic training data is either going to be badly biased, or be limited to such banal “logical” output that the model it trains will be entirely unable to process natural language input and you’ll need to specify your questions in something more precise. At which point, we’re back to traditional programming languages, only with several layers of unnecessary and expensive processing going on in between.

If it had that problem, existing models wouldn't have been able to learn how to code at the same time as understanding naural language.

At they limited? Sure. But not in the way you're saying here.

What they are trying to do is use an expensive inference strategy that results in better performance than standard inference and then try to create a model that performs like the expensive inference strategy "out of the factory" so to speak. I doubt that it will result in a huge performance increase, but it might be just enough to stay ahead of the competition.

Ollama 3.1 405B model already uses synthetic data for SFT. They basically trained a bunch of models to generate, correct, and reject synthetic data for various tasks. They cover it in section 4.3 in the "The Llama 3 Herd of Models" paper.

Humans use synthetic data of their own to help with learning. Reasoning, imagination, armchair philosophy, theoreticizing, and neurosciences talk about dream repetition. If synthetic daa is bad or flawed its only temporary until the technique gets further honed.

There's a huge difference between "the AI receives synthetic data as if from an omniscient being and proceeds as if it were true" and "the animal brain creates synthetic data and assesses its plausibility and downstream consequences."

Synthetic data in AI training in fact has nothing to do with dreaming or armchair theorizing. It's a ridiculous comparison.

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