[altruios]

How do you verify the work of training without redoing the exact same work for training? (That's the neat part: you don't)

Bitcoin is trust-solved because of how the new blocks depends on previous blocks. With training data, there is no such verification (prompts/answers pairs do not depend at all on other prompt/answer pairs) (if there was, we wouldn't need to do the work of training the data in the first place).

You can rely on multiplying the work where gross variations are ignored (as you suggest): but that will take a lot more overhead in compute, and still is susceptible to bad actors (but much more resistant).

There is no solid/good solution - afaik - for distributed training of an AI (Open assistant I think is working on open training data?), if there is: I'll sign up.

[hackpert]

There has been some interesting work when it comes to distributed training. For example DiLoCo (https://arxiv.org/abs/2311.08105). I also know that Bittensor and nousresearch collaborated on some kind of competitive distributed model frankensteining-training thingy that seems to be going well. https://bittensor.org/bittensor-and-nous-research/

Of course it gets harder as models get larger but distributed training doesn't seem totally infeasible. For example if we were to talk about MoE transformer models, perhaps separate slices of the model can be trained in an asynchronous manner and then combined with some retraining. You can have minimal regular communication about say, mean and variance for each layer and a new loss term dependent on these statistics to keep the "expertise" for each contributor distinct.