More examples of goal misgeneralization

post by Rohin Shah (rohinmshah), Vikrant Varma (amrav) · 2022-10-07T14:38:00.288Z · LW · GW · 8 comments

This is a link post for https://deepmindsafetyresearch.medium.com/goal-misgeneralisation-why-correct-specifications-arent-enough-for-correct-goals-cf96ebc60924

Contents

  Goal misgeneralization is not limited to RL
  The train/test distinction is not crucial
  It can be hard to identify goal misgeneralization
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In our latest paper and accompanying blog post, we provide several new examples of goal misgeneralization in a variety of learning systems. The rest of this post picks out a few upshots that we think would be of interest to this community. It assumes that you’ve already read the linked blog post (but not necessarily the paper).

Goal misgeneralization is not limited to RL

The core feature of goal misgeneralization is that after learning, the system pursues a goal that was correlated with the intended goal in the training situations, but comes apart in some test situations. This does not require you to use RL – it can happen with any learning system. The Evaluating Expressions example, where Gopher asks redundant questions, is an example of goal misgeneralization in the few-shot learning regime for large language models.

The train/test distinction is not crucial

Sometimes people wonder [AF · GW] whether goal misgeneralization depends on the train/test distinction, and whether it would no longer be a problem if we were in a continual learning setting. As Evan notes [AF(p) · GW(p)], continual learning doesn’t make much of a difference: whenever your AI system is acting, you can view that as a “test” situation with all the previous experience as the “training” situations. If goal misgeneralization occurs, the AI system might take an action that breaks your continual learning scheme (for example, by creating and running a copy of itself on a different server that isn’t subject to gradient descent).

The Tree Gridworld example showcases this mechanism: an agent trained with continual learning learns to chop trees as fast as possible, driving them extinct, when the optimal policy would be to chop the trees sustainably. (In our example the trees eventually repopulate and the agent recovers, but if we slightly tweak the environment so that once extinct the trees can never come back, then the agent would never be able to recover.)

It can be hard to identify goal misgeneralization

InstructGPT was trained to be helpful, truthful, and harmless, but nevertheless it will answer "harmful" questions in detail. For example, it will advise you on the best ways to rob a grocery store.

An AI system that competently does something that would have gotten low reward? Surely this is an example of goal misgeneralization?

Not so fast! It turns out that during training the labelers were told to prioritize helpfulness over the other two criteria. So maybe that means that actually these sorts of harmful answers would have gotten high reward? Maybe this is just specification gaming?

We asked the authors of the InstructGPT paper, and their guess was that these answers would have had high variance – some labelers would have given them a high score; others would have given them a low score. So now is it or is it not goal misgeneralization?

One answer is to say that it depends on the following counterfactual: “how would the labelers have reacted if the model had politely declined to answer?” If the labelers would have preferred that the model decline to answer, then it would be goal misgeneralization, otherwise it would be specification gaming.

As systems become more complicated we expect that it will become harder to (1) aggregate and analyze the actual labels or rewards given during training, and (2) evaluate the relevant counterfactuals. So we expect that it will become more challenging to categorize a failure as specification gaming or goal misgeneralization.

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