TL;DR: The Sharp Left Turn and specifically the alignment generalization ability is highly dependent on how much slack you allow between each optimisation epoch. By minimizing the slack you allow in the “utility boundary” (the part of the local landscape that is counted as part of you when trying to describe a utility function of the system) you can minmize the expected divergence of the optimization process and therefore minimize the alignment-capability gap?
A bit longer from the better half of me (Claude):
Your analysis of the sharp left turn made me reflect on an interesting angle regarding optimization processes and coordination. I’d like to share a framework that builds on but extends beyond your discussion of the (1-3) triad and capabilities generalization:
I believe we can understand sharp left turns through the lens of ‘optimization slack’ - the degree of freedom an optimization process has between correction points. Consider:
Modern ML systems use gradient descent with tight feedback loops and minimal slack
Evolution operated with enormous slack between meaningful corrections
Cultural evolution introduced intermediate coordination mechanisms through shared values
This connects to your discussion of autonomous learning and discernment, but examines it through a different lens. When you describe how ‘capabilities generalize further than alignment,’ I wonder if the key variable isn’t just the generalization itself, but how much slack we permit in the system before correction.
A concrete model I’ve been working with looks at this as boundaries in optimization space [see figure below].
Here’s some pictures from a comment that I left on one of the posts:
Which can be perceived as something like this in the environmental sense:
The hypothesis is that by carefully constraining the ‘utility boundary’ - the region within which a system can optimize while still being considered aligned—we might better control divergence.
I’m curious whether you see this framework as complementary to or in tension with your analysis of the (1-3) triad. Does thinking about alignment in terms of permitted optimization slack add useful nuance to the capabilities vs alignment generalization debate?
I don’t understand your comment but it seems vaguely related to what I said in §5.1.1.
Yeah, if we make the (dubious) assumption that all AIs at all times will have basically the same ontologies, same powers, and same ways of thinking about things, as their human supervisors, every step of the way, with continuous re-alignment, then IMO that would definitely eliminate sharp-left-turn-type problems, at least the way that I define and understand such problems right now.
Of course, there can still be other (non-sharp-left-turn) problems, like maybe the technical alignment approach doesn’t work for unrelated reasons (e.g. 1,2), or maybe we die from coordination problems (e.g.), etc.
Modern ML systems use gradient descent with tight feedback loops and minimal slack
I’m confused; I don’t know what you mean by this. Let’s be concrete. Would you describe GPT-o1 as “using gradient descent with tight feedback loops and minimal slack”? What about AlphaZero? What precisely would control the “feedback loop” and “slack” in those two cases?
Thank you for being patient with me, I tend to live in my own head a bit with these things :/ Let me know if this explanation is clearer using the examples you gave:
Let me build on the discussion about optimization slack and sharp left turns by exploring a concrete example that illustrates the key dynamics at play.
Think about the difference between TD-learning and Monte Carlo methods in reinforcement learning. In TD-learning, we update our value estimates frequently based on small temporal differences between successive states. The “slack”—how far we let the system explore/optimize between validation checks—is quite tight. In contrast, Monte Carlo methods wait until the end of an episode to make updates, allowing much more slack in the intermediate steps.
This difference provides insight into the sharp left turn problem. When we allow more slack between optimization steps (like in Monte Carlo methods), the system has more freedom to drift from its original utility function before course correction. The divergence compounds particularly when we have nested optimization processes—imagine a base model with significant slack that then has additional optimization layers built on top, each with their own slack. The total divergence potential multiplies.
This connects directly to your point about GPT-style models versus AlphaZero. While the gradient descent itself may have tight feedback loops, the higher-level optimization occurring through prompt engineering or fine-tuning introduces additional slack. It’s similar to how cultural evolution, with its long periods between meaningful corrections, allowed for the emergence of inner optimizers that could significantly diverge from the original selection pressures.
I’m still working to formalize precisely what mathematical structure best captures this notion of slack—whether it’s best understood through the lens of utility boundaries, free energy, or some other framework.
Epistemic status: Curious.
TL;DR:
The Sharp Left Turn and specifically the alignment generalization ability is highly dependent on how much slack you allow between each optimisation epoch. By minimizing the slack you allow in the “utility boundary” (the part of the local landscape that is counted as part of you when trying to describe a utility function of the system) you can minmize the expected divergence of the optimization process and therefore minimize the alignment-capability gap?
A bit longer from the better half of me (Claude):
Your analysis of the sharp left turn made me reflect on an interesting angle regarding optimization processes and coordination. I’d like to share a framework that builds on but extends beyond your discussion of the (1-3) triad and capabilities generalization:
I believe we can understand sharp left turns through the lens of ‘optimization slack’ - the degree of freedom an optimization process has between correction points. Consider:
Modern ML systems use gradient descent with tight feedback loops and minimal slack
Evolution operated with enormous slack between meaningful corrections
Cultural evolution introduced intermediate coordination mechanisms through shared values
This connects to your discussion of autonomous learning and discernment, but examines it through a different lens. When you describe how ‘capabilities generalize further than alignment,’ I wonder if the key variable isn’t just the generalization itself, but how much slack we permit in the system before correction.
A concrete model I’ve been working with looks at this as boundaries in optimization space [see figure below].
Here’s some pictures from a comment that I left on one of the posts:
Which can be perceived as something like this in the environmental sense:
The hypothesis is that by carefully constraining the ‘utility boundary’ - the region within which a system can optimize while still being considered aligned—we might better control divergence.
I’m curious whether you see this framework as complementary to or in tension with your analysis of the (1-3) triad. Does thinking about alignment in terms of permitted optimization slack add useful nuance to the capabilities vs alignment generalization debate?
I don’t understand your comment but it seems vaguely related to what I said in §5.1.1.
Yeah, if we make the (dubious) assumption that all AIs at all times will have basically the same ontologies, same powers, and same ways of thinking about things, as their human supervisors, every step of the way, with continuous re-alignment, then IMO that would definitely eliminate sharp-left-turn-type problems, at least the way that I define and understand such problems right now.
Of course, there can still be other (non-sharp-left-turn) problems, like maybe the technical alignment approach doesn’t work for unrelated reasons (e.g. 1,2), or maybe we die from coordination problems (e.g.), etc.
I’m confused; I don’t know what you mean by this. Let’s be concrete. Would you describe GPT-o1 as “using gradient descent with tight feedback loops and minimal slack”? What about AlphaZero? What precisely would control the “feedback loop” and “slack” in those two cases?
Thank you for being patient with me, I tend to live in my own head a bit with these things :/ Let me know if this explanation is clearer using the examples you gave:
Let me build on the discussion about optimization slack and sharp left turns by exploring a concrete example that illustrates the key dynamics at play.
Think about the difference between TD-learning and Monte Carlo methods in reinforcement learning. In TD-learning, we update our value estimates frequently based on small temporal differences between successive states. The “slack”—how far we let the system explore/optimize between validation checks—is quite tight. In contrast, Monte Carlo methods wait until the end of an episode to make updates, allowing much more slack in the intermediate steps.
This difference provides insight into the sharp left turn problem. When we allow more slack between optimization steps (like in Monte Carlo methods), the system has more freedom to drift from its original utility function before course correction. The divergence compounds particularly when we have nested optimization processes—imagine a base model with significant slack that then has additional optimization layers built on top, each with their own slack. The total divergence potential multiplies.
This connects directly to your point about GPT-style models versus AlphaZero. While the gradient descent itself may have tight feedback loops, the higher-level optimization occurring through prompt engineering or fine-tuning introduces additional slack. It’s similar to how cultural evolution, with its long periods between meaningful corrections, allowed for the emergence of inner optimizers that could significantly diverge from the original selection pressures.
I’m still working to formalize precisely what mathematical structure best captures this notion of slack—whether it’s best understood through the lens of utility boundaries, free energy, or some other framework.