In terms of content, this has a lot of overlap with Reward is not the optimization target. I'm basically rewriting a part of that post in language I personally find clearer, emphasising what I think is the core insight.


When thinking about deception and RLHF training, a simplified threat model is something like this:

  • A model takes some actions.
  • If a human approves of these actions, the human gives the model some reward.
  • Humans can be deceived into giving reward in situations where they would otherwise not if they had more knowledge.
  • Models will take advantage of this so they can get more reward.
  • Models will therefore become deceptive.

Before continuing, I would encourage you to really engage with the above. Does it make sense to you? Is it making any hidden assumptions? Is it missing any steps? Can you rewrite it to be more mechanistically correct?

I believe that when people use the above threat model, they are either using it as shorthand for something else or they misunderstand how reinforcement learning works. Most alignment researchers will be in the former category. However, I was in the latter.

I was missing an important insight into how reinforcement learning setups are actually implemented. This lack of understanding led to lots of muddled thinking and general sloppiness on my part. I see others making the exact same mistake so I thought I would try and motivate a more careful use of language!

How Vanilla Reinforcement Learning Works

If I were to explain RL to my parents, I might say something like this:

  • You want to train your dog to sit.
  • You say "sit" and give your dog a biscuit if it sits.
  • Your dog likes biscuits, and over time it will learn it can get more biscuits by sitting when told to do so.
  • Biscuits have let you incentivise the behaviour you want.
  • We do the same thing with a computer by giving the computer "reward" when it does things we like. Over time, the computer will do more of the behaviour we like so it can get more reward.

Do you agree with this? Is this analogy flawed in any way?

I claim this is actually NOT how vanilla reinforcement learning works.
The framing above views models as "wanting" reward, with reward being something models "receive" on taking certain actions. What actually happens is this:

  • The model takes a series of actions (which we collect across multiple "episodes").
  • After collecting these episodes, we determine how good the actions in each episode are using a reward function.
  • We use gradient descent to alter the parameters of the model so the good actions will be more likely and the bad actions will be less likely when we next collect some episodes.

The insight is that the model itself never "gets" the reward. Reward is something used separately from the model/environment.

To motivate this, let's view the above process not from the vantage point of the overall training loop but from the perspective of the model itself. For the purposes of demonstration, let's assume the model is a conscious and coherent entity. From it's perspective, the above process looks like:

  • Waking up with no memories in an environment.
  • Taking a bunch of actions.
  • Suddenly falling unconscious.
  • Waking up with no memories in an environment.
  • Taking a bunch of actions.
  • and so on.....

The model never "sees" the reward. Each time it wakes up in an environment, its cognition has been altered slightly such that it is more likely to take certain actions than it was before.
Reward is the mechanism by which we select parameters, it is not something "given" to the model.

To (rather gruesomely) link this back to the dog analogy, RL is more like asking 100 dogs to sit, breeding the dogs which do sit and killing those which don't.  Overtime, you will have a dog that can sit on command. No dog ever gets given a biscuit.

The phrasing I find most clear is this: Reinforcement learning should be viewed through the lens of selection, not the lens of incentivisation.

Why Does This Matter?

The "selection lens" has shifted my alignment intuitions a fair bit.

Goal-Directedness
It has changed how I think about goal-directed systems. I had unconsciously assumed models were strongly goal-directed by default and would do whatever they could to get more reward.

It's now clearer that goal-directedness in models is not a certainty, but something that can be potentially induced by the training process. If a model is goal-directed with respect to some goal, it is because such goal-directed cognition was selected for. Furthermore, it should be obvious that any learned goal will not be "get more reward", but something else. The model doesn't even see the reward!

CoinRun
Langosco et al. found an interesting failure mode in CoinRun.

The set up is this:

  • Have an agent navigate environments with a coin always on the right-hand side.
  • Reward the model when it reaches the coin.

At train-time everything goes as you would expect. The agent will move to the right-hand side of the level and reach the coin.
However, if at test-time you move the coin so it is now on the left-hand side of the level, the agent will not navigate to the coin, but instead continue navigating to the right-hand side of the level.

When I first saw this result, my initial response was one of confusion before giving way to "Inner misalignment is real. We are in trouble."

Under the "reward as incentivization" framing, my rationalisation of the CoinRun behaviour was:

  • At train-time, the model "wants" to get the coin.
  • However, when we shift distribution at test-time, the model now "wants" to move to the right-hand side of the level.

(In hindsight, there were several things wrong with my thinking...)

Under the "reward as selection" framing, I find the behaviour much less confusing:

  • We use reward to select for actions that led to the agent reaching the coin.
  • This selects for models implementing the algorithm "move towards the coin".
  • However, it also selects for models implementing the algorithm "always move to the right".
  • It should therefore not be surprising you can end up with an agent that always moves to the right and not necessarily towards the coin.

Rewriting the Threat Model

Let's take another look at the simplified deception/RLHF threat model:

  • A model takes some actions.
  • If a human approves of these actions, the human gives the model some reward.
  • Humans can be deceived into giving reward in situations where they would otherwise not if they had more knowledge.
  • Models will take advantage of this so they can get more reward.
  • Models will therefore become deceptive.

This assumes that models "want" reward, which isn't true. I think this threat model is confounding two related but different failure cases, which I would rewrite as the following:

1. Selecting For Bad Behaviour

  • A model takes some actions.
  • A human assigns positive reward to actions they approve of.
  • RL makes such actions more likely in the future.
  • Humans may assign reward to behaviour where they would not if they had more knowledge.
  • RL will reinforce such behaviour.
  • RLHF can therefore induce cognition in models which is unintended and "reflectively unwanted".

2. Induced Goal-Directedness

  • Consider a hypothetical model that chooses actions by optimizing towards some internal goal which is highly correlated with the reward that would be assigned by a human overseer.
  • Obviously, RL is going to exhibit selection pressure towards such a model.
  • RLHF could then induce goal-directed cognition.
  • This model does now indeed "want" to score highly according to some internal metric.
  • One way of doing so is to be deceptive... etc etc

So failure cases such as deception are still very much possible, but I would guess a fair few people are confused about the concrete mechanisms by which deception can be brought about. I think this does meaningfully change how you should think about alignment. For instance, on rereading Ajeya Cotra's writing on situational awareness, I have gone from thinking that "playing the training game" is a certainty to something that could happen, but only after training somehow induces goal-directedness in the model.

One Final Exercise For the Reader

When reading about alignment, I now notice myself checking the following:

  1. Does the author ever refer to a model "being rewarded"?
  2. Does the author ever refer to a model taking action to "get reward"?
  3. If either of the above is true, can you rephrase their argument in terms of selection?
  4. Can you go further and rephrase the argument by completely tabooing the word "reward"?
  5. Does this exercise make the argument more or less compelling?

I have found going through the above to be a useful intuition-building exercise. Hopefully that will be the same for others!

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2 comments, sorted by Click to highlight new comments since: Today at 8:37 AM

Under the "reward as selection" framing, I find the behaviour much less confusing:

  • We use reward to select for actions that led to the agent reaching the coin.
  • This selects for models implementing the algorithm "move towards the coin".
  • However, it also selects for models implementing the algorithm "always move to the right".
  • It should therefore not be surprising you can end up with an agent that always moves to the right and not necessarily towards the coin.

 

I've been reconsidering the coin run example as well recently from a causal perspective, and your articulation helped me crystalize my thoughts. Building on these points above, it seems clear that the core issue is one of causal confusion: that is, the true causal model M is "move right" -> "get the coin" -> "get reward". However, if the variable of "did you get the coin" is effectively latent (because the model selection doesn't discriminate on this variable) then the causal model M is indistinguishable from M' which is "move right" -> "get reward" (which though it is not the true causal model governing the system, generates the same observational distribution).

In fact, the incorrect model M' actually has shorter description length, so it may be that here there is a bias against learning the true causal model. If so, I believe we have a compelling explanation for the coin runner phenomenon which does not require the existence of a mesa optimizer, and which does indicate we should be more concerned about causal confusion.

Curated. I think I had read a bunch of stuff pointing in this direction before, but somehow this post helped the concepts (i.e. the distinction between selecting for bad behavior and for goal-directedness) be a lot clearer in my mind.