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I don't think it can be so easily dismissed. AlphaZero contributed lots, for example reproducing opening theory from a blank slate. For chess that's significant

I don't think it can be so easily dismissed. AlphaZero contributed lots, for example reproducing opening theory from a blank slate. For chess that's significant. But we don't care about chess for this discussion about GPT. Self-learning and self-play would be interesting because it's an amplification factor. Some searching suggests some researchers have been thinking along these lines. Maybe this? https://arxiv.org/abs/2210.11610 https://arxiv.org/abs/2210.11610 Going for methods that don't require supervision

mtlmtlmtlmtl 4y ago
Yes, AlphaZero was a nice innovation in training methods, employing iterated distillation and amplification. Of course a game is the perfect application for it because you get the fitness function built in: just play the game. There does seem to be some research into applying the same methods to alignment problems, which is interesting. Unfortunately Google's naming scheme is kind of confusing. AlphaGo is the go engine that has some human knowledge built in. Then supposedly AlphaZero is the generalised thing that can be trained on go or chess or shogi etc. But AlphaZero trained on chess is also referred to as just AlphaZero? Whereas AlphaZero trained on Go is called AlphaZero Go... I should have been clearer on which I meant I guess, my response to your sibling should clear up what I meant though.