alt Hacker NewsBut theoretically, what would prevent the labs from RL'ing the models on "weight-space programming" tasks so that they get better at them? Perhaps we're just at the beginning, like how coding agents sucked 2 years ago.
It still seems like a promising approach for RSI, distillation, fine-tuning, and a whole host of other tasks.
Replies
jfyi • today at 3:51 PM
Yeah, this was my thought (as a layman) as well.
It seems some level of interpretability, even if it requires tooling, could result in the ability to spin out synthetic training data sets from other models in an automated fashion.
ex-aws-dude • today at 3:15 PM
They might even discover some way to program the weights proportional to an error signal
Now that would be crazy
➕ show 1 reply
The context.
It's impossible to train on raw document > 1MB (I don't know the exact limit, but there is one). You can't vectorize weights (or can you?!) and the output context is limited. So you can't train a big model on it's previous weight or others models weights.
The output context can be "extended" using agents but I doubt anything coherent may be outputted using that technique.