All of Sam Marks's Comments + Replies

Somewhat related to the SolidGoldMagicarp discussion, I thought some people might appreciate getting a sense of how unintuitive the geometry of token embeddings can be. Namely, it's worth noting that the tokens whose embeddings are most cosine-similar to a random vector in embedding space tend not to look very semantically similar to each other. Some examples:

v_1                 v_2             v_3
--------------------------------------------------
 characterized       Columb          determines
 Stra                1900           conserv
 Ire             

This, broadly-speaking, is also my best guess, but I'd rather phrase it as: larger LMs are better at making the personas they imitate "realistic" (in the sense of being more similar to the personas you encounter when reading webtext). So doing RLHF on a larger LM results in getting an imitation of a more realistic useful persona. And for the helpful chatbot persona that Anthropic's language model was imitating, one correlate of being more realistic was preferring not to be shut down.

(This doesn't obviously explain the results on sycophancy. I think for tha... (read more)

Regarding your points on agentic simulacra (which I assume means "agentic personas the language model ends up imitating"):

1) My best guess about why Anthropic's model expressed self-preservation desires is the same as yours: the model was trying to imitate some relatively coherent persona, this persona was agentic, and so it was more likely to express self-preservation desires.

2) But I'm pretty skeptical about your intuition that RLHF makes the "imitating agentic personas" problem worse. When people I've spoken to talk about conditioning-based alternatives... (read more)

In terms of being able to sample from the conditional, I don't think that the important constraint here is $\alpha +\beta +\gamma =1$. Rather, it seems that the important constraint is that our architecture can only sample from distributions of the form $\alpha N({\mu }_A,{\sigma }_A^2)+\beta N({\mu }_B,{\sigma }_B^2)+\gamma N({\mu }_C,{\sigma }_C^2)$; even allowing $\alpha ,\beta ,\gamma$ to be arbitrary real numbers, this will never be the same as either (a) the distribution produced by conditioning the base model on high persuasiveness, or (b) the distribution which maximizes expected persuasiveness - KL divergence from the base model.... (read more)

(The worked example in this comment was a joint effort with Eric Neyman and Drake Thomas.)

Here's a toy example in which we get worse Goodharting for RL than for filtering: suppose that our model has three submodules

• A, which tries to produce outputs which are both true and persuasive
• B, which tries to produce outputs which are true, but have no effect on persuasiveness
• C, which tries to produce outputs which are persuasive, but with no effect on truthiness.

Our model has parameters $\alpha ,\beta ,\gamma$ summing to 1 which determine how much to listen to each of thes... (read more)

The paper is frustratingly vague about what their context lengths are for the various experiments, but based off of comparing figures 7 and 4, I would guess that the context length for Watermaze was 1-2 times as long as an episode length(=50 steps). (It does indeed look like they were embedding the 2d dark room observations into a 64-dimensional space, which is hilarious.)

I'm not sure I understand your second question. Are you asking about figure 4 in the paper (the same one I copied into this post)? There's no reward conditioning going on. They're also no... (read more)

My recent post on generative models has some related discussion; see especially remark 1 on the satisficer, quantilizer, and optimizer approaches to making agents with generative models.

Two interesting differences between the approaches discussed here and in my linked post:

• In my post, I assumed that the generative model was trained on a data set which included rewards (for example, humans playing Breakout, where the reward is provided by the environment; or a setting in which rewards can be provided by a reward model trained with human feedback). In contra

When "List of Lethalities" was posted, I privately wrote a list of where I disagreed with Eliezer, and I'm quite happy to see that there's a lot of convergence between my private list and Paul's list here. 

I thought it would be a useful exercise to diff my list with Paul's; I'll record the result in the rest of this comment without the expectation that it's useful to anyone else.

Points on both lists:

• Eliezer's "first critical try" framing downplays the importance of trial-and-error with non-critical tries.
• It's not clear that a "pivotal act" by an align

Hmm, I'm not sure I understand -- it doesn't seem to me like noisy observations ought to pose a big problem to control systems in general.

For example, suppose we want to minimize the number of mosquitos in the U.S., and we access to noisy estimates of mosquito counts in each county. This may result in us allocating resources slightly inefficiently (e.g. overspending resources on counties that have fewer mosquitos than we think), but we'll still always be doing the approximately correct thing and mosquito counts will go down. In particular, I don't see a se... (read more)

This paper gives a mathematical model of when Goodharting will occur. To summarize: if

(1) a human has some collection $s_1,\dots ,s_n$ of things which she values,

(2) a robot has access to a proxy utility function which takes into account some strict subset of those things, and

(3) the robot can freely vary how much of $s_1,\dots ,s_n$ there are in the world, subject only to resource constraints that make the $s_i$ trade off against each other,

then when the robot optimizes for its proxy utility, it will minimize all $s_i$'s which its proxy utility... (read more)

It seems to me that the meaning of the set $C$ of cases drifts significantly from when it is first introduced and the "Implications" section. It further seems to me that clarifying what exactly $C$ is supposed to be resolves the claimed tension between the existence of iterably improvable ontology identifiers and difficulty of learning human concept boundaries.

Initially, $C$ is taken to be a set of cases such that the question $Q$ has an objective, unambiguous answer. Cases where the meaning of $Q$ are ambiguous are ... (read more)