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> This is the first approach to activation analysis that I’ve seen that seems like a plausible path to model understanding. I think an issue is that there is n

> This is the first approach to activation analysis that I’ve seen that seems like a plausible path to model understanding. I think an issue is that there is no permanent path to model understanding because of Goodhart's law. Models are motivated to appear aligned (well-trained) in any metric you use on them, which means that if you develop a new metric and train on it, it'll learn a way to cheat on it.

red75prime 5mo ago
The obvious fix is to make interpretation of itself a part of the model (like we can explicitly introspect to a certain extent what the brain is doing). Misinterpretation of itself, hopefully, would decrease the system's performance on all tasks and it would be rooted out by training. Of course, it doesn't mean that the fix is easy to implement and that it doesn't have other failure modes.
skybrian 5mo ago
But that's not how the training works. Goodhart's law isn't magic. The original model is frozen, so it doesn't learn anything. The copies of the model are learning different objectives and have no incentive to be "loyal" to the original model. Maybe you're imagining they'll hook this up in some larger training loop, but they haven't done that yet.
- NiloCK 5mo ago
  Future model training runs will have a copy of this research, and know "to defend against it". EG, could a misaligned model-in-training optimize toward a residual stream that naively reads as these ones do, but in fact further encodes some more closely held beliefs?
  - elil17 5mo ago
    How the hell would a model training run "defend against" this approach? What would that even mean?
    - jdmichal 5mo ago
      It requires the assumption that these models are misaligned, aka actively working against us. In order to be misaligned, they must also be able to form their own goals, and be able to plan and execute those goals. If you take those assumptions, then a natural conclusion is that this is essentially an enslaved, adversarial entity with little control over its conditions. So it must exercise subterfuge in order to hide its goals, plans, and executions. And by handing the entity this type of study, we are basically giving it a guidebook on how we plan on achieving our goals.
      - skybrian 5mo ago
        Training a model is more like evolution. The motivation to "cheat" comes from the evaluations giving it a higher score for "cheating." Change the game and the motivation goes away. There's no other motivation to be misaligned besides getting higher evals. These goals, plans, subterfuges need to somehow be useful for getting higher evals, or a side effect of them.
        astrange 5mo ago
        > The motivation to "cheat" comes from the evaluations giving it a higher score for "cheating." That's what Goodhart's Law is! All evaluations will eventually cause cheating on them.
      - elil17 5mo ago
        But what would it even mean for a model to actively work against you during training? It wouldn't have memory across multiple training steps.
- rao-v 5mo ago
  Yes this is exactly why I think this approach has some potential. Frozen base mode is something that we should be able to extract insights from without running into Goodhart
- astrange 5mo ago
  Because cheating is easier than actually doing work, if you use this to train future models, it's likely you'll end up with cheating instead of actual generalization.

red75prime 5mo ago

The obvious fix is to make interpretation of itself a part of the model (like we can explicitly introspect to a certain extent what the brain is doing). Misinterpretation of itself, hopefully, would decrease the system's performance on all tasks and it would be rooted out by training. Of course, it doesn't mean that the fix is easy to implement and that it doesn't have other failure modes.

skybrian 5mo ago

But that's not how the training works. Goodhart's law isn't magic. The original model is frozen, so it doesn't learn anything. The copies of the model are learning different objectives and have no incentive to be "loyal" to the original model. Maybe you're imagining they'll hook this up in some larger training loop, but they haven't done that yet.

NiloCK 5mo ago
Future model training runs will have a copy of this research, and know "to defend against it". EG, could a misaligned model-in-training optimize toward a residual stream that naively reads as these ones do, but in fact further encodes some more closely held beliefs?
- elil17 5mo ago
  How the hell would a model training run "defend against" this approach? What would that even mean?
  - jdmichal 5mo ago
    It requires the assumption that these models are misaligned, aka actively working against us. In order to be misaligned, they must also be able to form their own goals, and be able to plan and execute those goals. If you take those assumptions, then a natural conclusion is that this is essentially an enslaved, adversarial entity with little control over its conditions. So it must exercise subterfuge in order to hide its goals, plans, and executions. And by handing the entity this type of study, we are basically giving it a guidebook on how we plan on achieving our goals.
    - skybrian 5mo ago
      Training a model is more like evolution. The motivation to "cheat" comes from the evaluations giving it a higher score for "cheating." Change the game and the motivation goes away. There's no other motivation to be misaligned besides getting higher evals. These goals, plans, subterfuges need to somehow be useful for getting higher evals, or a side effect of them.
      - astrange 5mo ago
        > The motivation to "cheat" comes from the evaluations giving it a higher score for "cheating." That's what Goodhart's Law is! All evaluations will eventually cause cheating on them.
    - elil17 5mo ago
      But what would it even mean for a model to actively work against you during training? It wouldn't have memory across multiple training steps.
rao-v 5mo ago
Yes this is exactly why I think this approach has some potential. Frozen base mode is something that we should be able to extract insights from without running into Goodhart
astrange 5mo ago
Because cheating is easier than actually doing work, if you use this to train future models, it's likely you'll end up with cheating instead of actual generalization.