Quick Takes

I view this post as providing value in three (related) ways: 1. Making a pedagogical advancement regarding the so-called inner alignment problem 2. Pointing out that a common view of "RL agents optimize reward" is subtly wrong 3. Pushing for thinking mechanistically about cognition-updates   Re 1: I first heard about the inner alignment problem through Risks From Learned Optimization and popularizations of the work. I didn't truly comprehend it - sure, I could parrot back terms like "base optimizer" and "mesa-optimizer", but it didn't click. I was confused. Some months later I read this post and then it clicked. Part of the pedagogical value is not having to introduce the 4 terms of form [base/mesa] + [optimizer/objective] and throwing those around. Even with Rob Miles' exposition skills that's a bit overwhelming. Another part I liked were the phrases "Just because common English endows “reward” with suggestive pleasurable connotations" and "Let’s strip away the suggestive word “reward”, and replace it by its substance: cognition-updater." One could be tempted to object and say that surely no one would make the mistakes pointed out here, but definitely some people do. I did. Being a bit gloves off here definitely helped me.   Re 2: The essay argues for, well, reward not being the optimization target. There is some deep discussion in the comments about the likelihood of reward in fact being the optimization target, or at least quite close (see here). Let me take a more shallow view. I think there are people who think that reward is the optimization target by definition or by design, as opposed to this being a highly non-trivial claim that needs to be argued for. It's the former view that this post (correctly) argues against. I am sympathetic to pushback of the form "there are arguments that make it reasonable to privilege reward-maximization as a hypothesis" and about this post going a bit too far, but these remarks should not be confused with a rebuttal

Retrospective: I think this is the most important post I wrote in 2022. I deeply hope that more people benefit by fully integrating these ideas into their worldviews. I think there's a way to "see" this lesson everywhere in alignment: for it to inform your speculation about everything from supervised fine-tuning to reward overoptimization. To see past mistaken assumptions about how learning processes work, and to think for oneself instead. This post represents an invaluable tool in my mental toolbelt. I wish I had written the key lessons and insights more plainly. I think I got a bit carried away with in-group terminology and linguistic conventions, which limited the reach and impact of these insights. I am less wedded to "think about what shards will form and make sure they don't care about bad stuff (like reward)", because I think we won't get intrinsically agentic policy networks. I think the most impactful AIs will be LLMs+tools+scaffolding, with the LLMs themselves being "tool AI."

I view this post as providing value in three (related) ways: 1. Making a pedagogical advancement regarding the so-called inner alignment problem 2. Pointing out that a common view of "RL agents optimize reward" is subtly wrong 3. Pushing for thinking mechanistically about cognition-updates   Re 1: I first heard about the inner alignment problem through Risks From Learned Optimization and popularizations of the work. I didn't truly comprehend it - sure, I could parrot back terms like "base optimizer" and "mesa-optimizer", but it didn't click. I was confused. Some months later I read this post and then it clicked. Part of the pedagogical value is not having to introduce the 4 terms of form [base/mesa] + [optimizer/objective] and throwing those around. Even with Rob Miles' exposition skills that's a bit overwhelming. Another part I liked were the phrases "Just because common English endows “reward” with suggestive pleasurable connotations" and "Let’s strip away the suggestive word “reward”, and replace it by its substance: cognition-updater." One could be tempted to object and say that surely no one would make the mistakes pointed out here, but definitely some people do. I did. Being a bit gloves off here definitely helped me.   Re 2: The essay argues for, well, reward not being the optimization target. There is some deep discussion in the comments about the likelihood of reward in fact being the optimization target, or at least quite close (see here). Let me take a more shallow view. I think there are people who think that reward is the optimization target by definition or by design, as opposed to this being a highly non-trivial claim that needs to be argued for. It's the former view that this post (correctly) argues against. I am sympathetic to pushback of the form "there are arguments that make it reasonable to privilege reward-maximization as a hypothesis" and about this post going a bit too far, but these remarks should not be confused with a rebuttal

Retrospective: I think this is the most important post I wrote in 2022. I deeply hope that more people benefit by fully integrating these ideas into their worldviews. I think there's a way to "see" this lesson everywhere in alignment: for it to inform your speculation about everything from supervised fine-tuning to reward overoptimization. To see past mistaken assumptions about how learning processes work, and to think for oneself instead. This post represents an invaluable tool in my mental toolbelt. I wish I had written the key lessons and insights more plainly. I think I got a bit carried away with in-group terminology and linguistic conventions, which limited the reach and impact of these insights. I am less wedded to "think about what shards will form and make sure they don't care about bad stuff (like reward)", because I think we won't get intrinsically agentic policy networks. I think the most impactful AIs will be LLMs+tools+scaffolding, with the LLMs themselves being "tool AI."