Jacob Hilton

# Wiki Contributions

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Yes, I think the most natural way to estimate total surprise in practice would be to use sampling like you suggest. You could try to find the best explanation for "the model does $bad_thing with probability less than 1 in a million" (which you believe based on sampling) and then see how unlikely$bad_thing is according to the resulting explanation. In the Boolean circuit worked example, the final 23-bit explanation is likely still the best explanation for why the model outputs TRUE on at least 99% of inputs, and we can use this explanation to see that the model actually outputs TRUE on all inputs.

Another possible approach is analogous to fine-tuning. You could start by using surprise accounting to find the best explanation for "the loss of the model is L" (where L is estimated during training), which should incentivize rich explanations of the model's behavior in general. Then to estimate the probability that model does some rare \$bad_thing, you could "fine-tune" your explanation using an objective that encourages it to focus on the relevant tails of the distribution. We have more ideas about estimating the probability of events that are too rare to estimate via sampling, and have been considering objectives other than surprise accounting for this. We plan to share these ideas soon.

Since this post was written, OpenAI has done much more to communicate its overall approach to safety, making this post somewhat obsolete. At the time, I think it conveyed some useful information, although it was perceived as more defensive than I intended.

My main regret is bringing up the Anthropic split, since I was not able to do justice to the topic. I was trying to communicate that OpenAI maintained its alignment research capacity, but should have made that point without mentioning Anthropic.

Ultimately I think the post was mostly useful for sparking some interesting discussion in the comments.

I think KL/entropy regularization is usually used to prevent mode collapse partly because it has nice theoretical properties. In particular, it is easy to reason about the optimal policy for the regularized objective - see for example the analysis in the paper Equivalence Between Policy Gradients and Soft Q-Learning.

Nevertheless, action-dependent baselines do appear in the literature, although the story is a bit confusing. This is my understanding of it from some old notes:

• The idea was explored in Q-Prop. But unlike you, their intention was not to change the optimal policy, but rather to reduce the variance of the policy gradient. Therefore they also incorporated an additional term to cancel out the bias introduced by the action-dependent baseline. (Incidentally, perhaps this analysis is also relevant to understanding ACTDE.)
• Later, The Mirage of Action-Dependent Baselines showed that in fact the variance reduction due the action-dependent baseline was negligible, and the entire benefit of Q-Prop was essentially due to a bug! The implementation normalized advantage estimates, but failed to apply the same adjustment to the bias-correction term, which turned out to be independently helpful because it's essentially the DDPG training objective.

The questions on the take-home test vary in difficulty but are generally easier than olympiad problems, and should be accessible to graduates with relevant background. However, it is important to note that we are ultimately interested in research ability rather than the ability to solve self-contained problems under timed conditions. So although the take-home test forms part of our assessment, we also look at other signals such as research track-record (while recognizing that assessing research ability is unfortunately very hard).

(Note: I am talking about the current version of the test, it's possible that the difficulty will change as we refine our interview process.)

I think the direction depends on what your expectations were – I'll try to explain.

First, some terminology: the term "horizon length" is used in the paper to refer to the number of timesteps over which the algorithm pays attention to rewards, as governed by the discount rate. In the biological anchors framework, the term "effective horizon length" is used to refer to a multiplier on the number of samples required to train the model, which is influenced by the horizon length and other factors. For clarity, I'll using the term "scaling multiplier" instead of "effective horizon length" in this comment. The paper studies the effect of the horizon length on the scaling multiplier in a toy MNIST setting.

One key takeaway is that the scaling multiplier is not simply proportional to the horizon length, as one might have naively expected. Instead, the number of samples required is the sum of two components, one that is inherent to the task and independent of the horizon length, and one that is proportional to the horizon length. Compared to the naive expectation, this means that training compute requirements are lower. On the other hand, this ignores reward sparsity, so you might expect training compute requirements to be higher once both horizon length and reward sparsity are accounted for.

The paper also lends some support to the modeling assumptions of the neural network anchor, by validating the hypotheses that (a) training compute requirements still scale as a power law in model size for reinforcement learning, and with a similar exponent, and (b) the scaling multiplier can indeed vary a lot between environments. This might make you put more weight on the neural network anchor, which could again have either directional effect.

The other takeaways are more methodological and I don't think have much of a directional effect.

1. We are just observing that the gold RM score curves in Figure 9 overlap. In other words, the KL penalty did not affect the relationship between KL and gold RM score in this experiment, meaning that any point on the Pareto frontier could be reached using only early stopping, without the KL penalty. As mentioned though, we've observed this result to be sensitive to hyperparameters, and so we are less confident in it than other results in the paper.
2. I don't have this data to hand unfortunately.
3. I don't have this data to hand, but entropy typically falls roughly linearly over the course of training, sometimes slightly faster towards the start, and typically moving around more than KL. So I'd expect the graph to look somewhat similar, but for it to be noisier and for the functional form to not fit as well.

Agreed. Likewise, in a transformer, the token dimension should maintain some relationship with the input and output tokens. This is sometimes taken for granted, but it is a good example of the data preferring a coordinate system. My remark that you quoted only really applies to the channel dimension, across which layers typically scramble everything.

The notion of a preferred (linear) transformation for interpretability has been called a "privileged basis" in the mechanistic interpretability literature. See for example Softmax Linear Units, where the idea is discussed at length.

In practice, the typical reason to expect a privileged basis is in fact SGD – or more precisely, the choice of architecture. Specifically, activation functions such as ReLU often privilege the standard basis. I would not generally expect the data or the initialization to privilege any basis beyond the start of the network or the start of training. The data may itself have a privileged basis, but this should be lost as soon as the first linear layer is reached. The initialization is usually Gaussian and hence isotropic anyway, but if it did have a privileged basis I would also expect this to be quickly lost without some other reason to hold onto it.

For people viewing on the Alignment Forum, there is a separate thread on this question here. (Edit: my link to LessWrong is automatically converted to an Alignment Forum link, you will have to navigate there yourself.)

Without commenting on the specifics, I have edited to the post to mitigate potential confusion: "this fact alone is not intended to provide a complete picture of the Anthropic split, which is more complicated than I am able to explain here".