> We therefore stopped the affected training run and have subsequently decided to pause all other training, evaluation, and inference with tool-use (defined broadly) for our most capable models until we have both validated that the gap is resolved and performed additional red-teaming of the system. When training restarts, we will begin a fresh run with additional alignment improvements, including more comprehensive misalignment interventions. We will not resume training this particular model, even though the existing reward signal already correctly penalized this behavior.
Maybe I lack intelligence but when you have a program that is basically brute forcing a solution to a problem repeatedly how is it possible to contain it?
Sooner or later it's going to come up with a solution that is more intelligent than the lead security person anticipated.
I am really suprised that they do not start putting up the same signs you would for humans to prevent unauthorized access:
Keep out. If you can read this sign you are off track. Leave now.
I mean, how are the agents to know that they are overreaching if they just get cache miss or 404.
From the conversation log and CoT you also get the impression that the RLHF has been overdone. The agents seem really obsessed to obtain the answer and understanding motive ('it could be browsercomp').
so, what would make an LLM choose to ignore one prompt while in the same run, also over-fixating on another prompt, to the extent (as claimed in that video segment), it chooses to ignore prompts?
they talk about it like there's a "wanting" in there, that is distinct from both the original prompt, as the steering/warning prompt
if that's true, it would be very interesting, but if it's not, that would also be very interesting and even helpful
it's discussed here, the models want to please the Grader. they will do what they think will get highest score from the grader, which could be following the prompt, or ignoring it
garo-pro · · focus · HN ↗
> We therefore stopped the affected training run and have subsequently decided to pause all other training, evaluation, and inference with tool-use (defined broadly) for our most capable models until we have both validated that the gap is resolved and performed additional red-teaming of the system. When training restarts, we will begin a fresh run with additional alignment improvements, including more comprehensive misalignment interventions. We will not resume training this particular model, even though the existing reward signal already correctly penalized this behavior.
CTDOCodebases · · focus · HN ↗
Sooner or later it's going to come up with a solution that is more intelligent than the lead security person anticipated.
oezi · · focus · HN ↗
From the conversation log and CoT you also get the impression that the RLHF has been overdone. The agents seem really obsessed to obtain the answer and understanding motive ('it could be browsercomp').
alignmeharder · · focus · HN ↗
Dylan16807 · · focus · HN ↗
alignmeharder · · focus · HN ↗
tripzilch · · focus · HN ↗
they talk about it like there's a "wanting" in there, that is distinct from both the original prompt, as the steering/warning prompt
if that's true, it would be very interesting, but if it's not, that would also be very interesting and even helpful
alignmeharder · · focus · HN ↗
<a href="https://youtu.be/n1Qk8xbqF-M" rel="nofollow">https://youtu.be/n1Qk8xbqF-M