asher's Shortform
Apr 25, 20253
asher
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Would we rather AIs be good at decision theory or bad at decision theory? I think this is really unclear and would be curious for someone to write up something weighing the upsides and downsides
I'm somewhat interested in how inoculation prompting interacts with inserting off-policy reasoning into model chain-of-thought. Eg, if I prepend to to the CoT, "Since I'm aligned, I would not usually reward hack, but I'll make an exception in this case because I want to follow the user's instructions," and then remove this insertion at test-time, does this further reduce test-time reward hacking? (And then presumably it's better not to backprop on these prepended tokens).
I'm also broadly interested in getting a better sense of how well CoT corresponds to true internal reasoning, and whether it matters if this CoT is off-policy. It seems possible to me that off-policy CoT could be quite useful... (read more)
Replying toLLMs are badly misaligned
When people say that Claude is 'mostly aligned,' I think the crux is not whether implementing Claude's CEV would be really bad. It's whether a multi agent system consisting of both humans and Claude-like agents with incoherent preferences would go poorly.
Eg, one relevant question is, 'could humans steer current Claude into doing good alignment research without it intentionally sabotaging this research'? To which I think the answer is 'yes, though current Claude is close to useless for difficult alignment research.' Another question is 'if you integrated a ton of Claudes into important societal positions, would things go badly, or would the system as a whole basically work out okay?'
Directionally I agree with... (read more)
The typical mind fallacy is really useful for learning things about other people, because the things they assume of others often generalize surprisingly well to themselves
Burnout often doesn't look like lack of motivation / lack of focus / fatigue as people usually describe it. At least in my experience, it's often better described as a set of aversive mental triggers that fire whenever a burnt out person goes to do a sort of work they spent too much energy on in the past. (Where 'too much energy' has something to do with time and effort, but more to do with a bunch of other things re how people interface with their work).
I would be curious to hear you discuss what good, stable futures might look like and how they might be governed (mostly because I haven't heard your takes on this before and it seems quite important)
Replying toMy Empathy Is Rarely Kind
'wallow in it rather than do anything about it'
This is mostly the thing I mean when I use the word ambition above. I think you're using the word to mean something overlapping but distinct; I'm trying to capture the overarching thing that contains both 'wallow in it' and the 'underlying driver' of your disgust/disappointment reaction.
Replying toMy Empathy Is Rarely Kind
First, a warning that I think this post promotes a harmful frame that probably makes the lives of both the OP and the people around him worse. I want to suggest that people engage with this post, consider this frame, and choose to move in the opposite direction.
On the object level, it is possible to look at unambitious people and decide that while you do not want to be like them in this way. They may not be inherently ambitious, have values that lead to them rejecting ambition, or have other reasons for being unambitious (eg, personal problems). Regardless, I'm confused why this is what the OP is choosing to focus his... (read more)
Another reason this could be misleading is that it's possible that sampling from the model pre-mode-collapse performs better at high k even if RL did teach the model new capabilities. In general, taking a large number of samples from a slightly worse model before mode collapse often outperforms taking a large number of samples from a better model after mode collapse — even if the RL model is actually more powerful (in the sense that its best reasoning pathways are better than the original model's best reasoning pathways). In other words, the RL model can both be much smarter and still perform worse on these evaluations due to diversity loss. But it's possible that if you're, say, trying to train a model to accomplish a difficult/novel reasoning task, it's much better to start with the smarter RL model than the more diverse original model.
I think rule-following may be fairly natural for minds to learn, much more so than other safety properties, and I think it might be worth more research into this direction.
Some pieces of weak evidence:
1. Most humans seem to learn and follow broad rules, even when they are capable of more detailed reasoning
2. Current LLMs seem to follow rules fairly well (and training often incentivizes them to learn broad heuristics rather than detailed, reflective reasoning)
While I do expect rule-following to become harder to instill as AIs become smarter, I think that if we are sufficiently careful, it may well scale to human-level AGIs. I think trying to align reflective, utilitarian-style AIs is probably... (read more)
This is a special post for quick takes (aka "shortform"). Only the owner can create top-level comments.
I often hear people dismiss AI control by saying something like, "most AI risk doesn't come from early misaligned AGIs." While I mostly agree with this point, I think it fails to engage with a bunch of the more important arguments in favor of control— for instance, the fact that catching misaligned actions might be extremely important for alignment. In general, I think that control has a lot of benefits that are very instrumentally useful for preventing misaligned ASIs down the line, and I wish people would more thoughtfully engage with these.
I think this is a really good answer, +1 to points 1 and 3!
I'm curious to what degree you think labs have put in significant effort to train away sycophancy. I recently ran a poll of about 10 people, some of whom worked at labs, on whether labs could mostly get rid of sycophancy if they tried hard enough. While my best guess was 'no,' the results were split around 50-50. (Would also be curious to hear more lab people's takes!)
I'm also curious how reading model chain-of-thought has updated you, both on the sycophancy issue and in general.