Is evaluation of capabilities, which as you note requires fine-tuning and other such techniques, a realistic thing to properly do continuously during model training, without that being prohibitively slow or expensive? Would doing this be part of the intended RSP?
Anthropic’s RSP includes evals after every 4x increase in effective compute and after every 3 months, whichever comes sooner, even if this happens during training, and the policy says that these evaluations include fine-tuning.
Do you know why 4x was picked? I understand that doing evals properly is a pretty substantial effort, but once we get up to gigantic sizes and proto-AGIs it seems like it could hide a lot. If there was a model sitting in training with 3x the train-compute of GPT4 I’d be very keen to know what it could do!
Is evaluation of capabilities, which as you note requires fine-tuning and other such techniques, a realistic thing to properly do continuously during model training, without that being prohibitively slow or expensive? Would doing this be part of the intended RSP?
Anthropic’s RSP includes evals after every 4x increase in effective compute and after every 3 months, whichever comes sooner, even if this happens during training, and the policy says that these evaluations include fine-tuning.
Do you know why 4x was picked? I understand that doing evals properly is a pretty substantial effort, but once we get up to gigantic sizes and proto-AGIs it seems like it could hide a lot. If there was a model sitting in training with 3x the train-compute of GPT4 I’d be very keen to know what it could do!