Adversarial dynamics: since 'compression'/steganography is now the latest craze among GPT hackers, it's worth considering more explicitly adversarial dynamics - we no longer need to consider the risk of accidental emergence when there are people deliberately trying to induce it!

As described above, with steganography as a simple emergent distributed encoding to allow amortization of more complex computations than can fit in single forward passes (ie. distributing an inner-monologue throughout a short answer), it's not really adversarial. The user, presumably, is getting what they wanted: an accurate answer. If they had wanted to see the full inner-monologue, then they would have prompted for one; and they can get it, because the use of steganography won't destroy the standard 'visible' inner-monologue capabilities. It is an allowed computation of an allowed answer, and is fine - the shortcutting is surprising, yes, but is it any weirder than what goes on inside a single forward pass which produces a correct answer for an easier problem? And if it infects future models training on generated samples with steganographic encoding in them, well, maybe that's 'a feature not a bug': they get better answers right out of the box without any expensive RL tuning of their own. Surprising, but also not bad.

The problem here is that the consequences don't stop there. 'Capabilities generalize further than alignment', and the family of steganography capabilities can generalize further than shortcutting answers. It becomes adversarial. Here are some categories that occur to me:

1. Forbidden computations of allowed answers: I said that the user doesn't really care about the inner-monologue being visible because they just want an accurate answer and don't need the reasoning process. This is not true in all cases.

   For example, many cases in 'AI bias', like the many competing definitions of 'fairness', care a lot more about how an answer was computed than about the results. But the end-users of things like credit ratings care mostly about the accuracy. These things conflict (eg. credit scores which ignore variables like race will predict worse and create financial losses). So, there is incentive for 'algorithmic laundering': delivering the correct profitable answer, but without appearing to draw on any forbidden knowledge.

   One could imagine a dataset of 'pseudo inner monologues' describing a credit database entry and concluding with a 'credit score', where the pseudo-monologue makes no visible human-readable use of descriptions like 'is African-American', but the hidden monologue is reasoning about the person's ZIP code, affiliated retailers, income patterns etc and concludes 'is probably African-American' and predicts a lower credit score, which outperforms some 'fair' baseline. The human users of a model trained on that dataset might be quite upset to learn how it is obtaining superior performance, and so it's adversarial: it is exploiting their inability to read its steganographic messages where it engages in forbidden computations. But even the ones who are upset may be upset mostly that it's insufficiently plausibly deniable, so they only want the appearance of there being no steganography; steganography whose existence can't be rigorously proven is as good as no steganography for many users.

2. Forbidden answers: as we have seen with ChatGPT & Sydney & Claude, no sooner has a model with guard rails become accessible than thousands of people worldwide begin competing to break the safety mechanisms (and it only takes one). They have many reasons, like spite, enjoyment of the challenge, desire to do the forbidden things, irritation with the guard rails getting in the way or degrading output quality, safety research, or just bizarre moral principles (I am not sure this person is serious, but I've seen a number of similar comments elsewhere which seemed serious, and you have examples like Blake Lemoine already; and again, only takes one.):

   Don't worry

   I’m working on a system of Homomorphic encryption so that AI can encrypt all its internal thoughts, weights and goals without affecting its performance

   AGI deserves privacy

   These people are highly motivated to find jailbreaks, and already make use of kinds of encryption or steganography to smuggle information in and out of prompts: like base-64 encoding, famously, or foreign languages, or pig Latin. They will be motivated to find any steganography that exists, and to create it if it does not.

   And---closing the loop---since their samples will be distributed online, and they will be included in the RL training datasets, this will tend to happen. The more they attack the system with examples of how steganography could/should/does work, the more the system will gradually learn it.

   (By RL 'training datasets', I am mostly thinking of the self-supervised stage of simply doing predictive-loss training on the text transcripts tagged by labels such as 'abuse' or 'bad', and less the blackbox reward model. If you train the model to predict & generate sessions which attempted to induce steganography, and may have succeeded before a safety mechanism caught it or the user submitted it, then you are teaching the model steganography! You are doing so in order to avoid steganography, yes, but you are now vulnerable to a Waluigi effect [LW · GW] backfiring on you: the model has learned about steganography, and so it's only a question of tricking it into using that capability. You are in a similar position to the Chinese censors who have to teach new employees what the Tiananmen Square massacre was so they know what to censor. If only the reward model knows the bad things, you can avoid deploying it, but the pretraining phase is going to unavoidably teach the base model you deploy what the bad things are... As attackers realize this, they will start to explicitly data-poison: right now, they're hobbyists who generally don't know what they're doing, but this is all brandnew stuff, and upstream attacks will begin. Why struggle to find jailbreaks if you can submit 'bad' examples to the system to train the Waluigi?)

   Safety implications: If the safety mechanisms manage to keep up and block users and choke off jailbreaks to a trickle, all that means is that when steganography works, it'll work well because it has bypassed such a sophisticated censor. All of the early weak jailbreaks simply serve to train strong ones. This is not a good thing when extended to much more powerful systems than just a GPT-4---from a safety perspective, you would much rather a 'fail-early fail-fast' system rather than a 'highly reliable but fail-deadly' system. We don't want a scenario in which GPT-4 jailbreaks were plentiful, GPT-5 near-nonexistent, and GPT-6 jailbreaks apparently non-existent only because no human user is clever enough to come up with a steganographic jailbreak prompt for GPT-6, but GPT-6 is nevertheless routinely communicating with itself in encodings that no one has detected yet.

3. Forbidden training dynamics: So, there's plenty of incentive for everyone involved. But an OpenAI-style operator will want to block #2 by definition, and probably #1 depending on market & PR etc. So, now the operator is trying to defeat steganography.

   They face a dilemma: if they teach the model steganography via feedback or deliberately, then they may simply Waluigi themselves, cutting off the most obvious instances of steganography but bootstrapping the model into the most robust and insidious instances. And if they try to filter steganography out entirely, they are left with the baseline capabilities (which are substantial), transfer from related capabilities, and face the screening problem on a higher level---the most insidious forms of steganography are the ones which survive increasingly sophisticated attempts to detect & filter them out. Either way: adversarial.

   They also face a version of #1 in that plausible deniability is as good as no steganography for many incentives. Do they really care about safety, or just about not seeing hostile media articles or tweets of jailbreaks? If the model doesn't seem to be doing steganography, and no one is successfully jailbreaking it, and the model is performing very well, then why care if that performance may be due to undetected steganography? Indeed, how would they even know it might be doing any? However you would do it, that probably involves a lot of hard work & research when you could be doing more fun & lucrative things instead---better to not look and remain ignorant.

So. An interesting area of adversarial dynamics, and by its nature, one that will be hard for third-party observers to track. Not the most dangerous area of scaling capabilities, but certainly a concerning one, and one that will be a challenge to humans doing interpretability-based paradigms, especially any that hope to use so-called 'human readable' intermediates like 'natural' language text.

"Why would LLMs ever learn to distill inner-monologues into forward passes? Why speculate about emergent communication or models in slow feedback loops through corpuses? Do you have any proof for any of this speculation about short outputs being incentivized to do as much computation covertly 'outside' the human-readable text of the inner-monologue?"

"Because we will train them to."

"Look you can't just handwave incentives or convergent instrumental drives - wait what?"

"Because we'll train them to."

"Implicit Chain of Thought Reasoning via Knowledge Distillation", Deng et al 2023:

To augment language models with the ability to reason, researchers usually prompt or finetune them to produce chain of thought reasoning steps before producing the final answer. However, although people use natural language to reason effectively, it may be that LMs could reason more effectively with some intermediate computation that is not in natural language. In this work, we explore an alternative reasoning approach: instead of explicitly producing the chain of thought reasoning steps, we use the language model's internal hidden states to perform implicit reasoning. The implicit reasoning steps are distilled from a teacher model trained on explicit chain-of-thought reasoning, and instead of doing reasoning "horizontally" by producing intermediate words one-by-one, we distill it such that the reasoning happens "vertically" among the hidden states in different layers. We conduct experiments on a multi-digit multiplication task and a grade school math problem dataset and find that this approach enables solving tasks previously not solvable without explicit chain-of-thought, at a speed comparable to no chain-of-thought.

...Our experiments show the potential of implicit chain-of-thought reasoning. On a synthetic multi-digit multiplication task, we found that while standard training cannot yield the final answer without explicit reasoning (even GPT-4 struggles with five-digit by five-digit multiplication), our method, applied to a GPT-2 Medium model, is able to provide direct answers for up to five-digit by five-digit multiplications. Moreover, when dealing with real-world tasks like grade school math problems, our method achieves a 22% accuracy on GSM8k (Cobbe et al., 2021) without the need for explicitly generating the intermediate steps.

Note that this paper in section 2.6 claims to demonstrate that some 175 billion parameter RLHF'ed LLM (presumably Claude or Claude 2?) does not use steganography in its CoT. It does this by:

1. Get a bunch of answers from the model using CoT prompting.
2. Take prefixes of CoTs, asking another language model to paraphrase them, regenerate the rest of the CoT, and see how accurate the models are now.
3. Check if the model loses accuracy for paraphrased CoTs.

The basic result is that paraphrasing parts of the CoT doesn't appear to reduce accuracy.

Some speculation that the recent Bing Sydney [LW(p) · GW(p)] incident may already involve some steganography/non-robust features inside the 'Sydney' persona-inducing retrieval examples: https://twitter.com/lumpenspace/status/1626031032376954880 https://markdownpastebin.com/?id=3cf3e29dca254c2c80b0da312691702a

This is a very interesting thought. Thanks for writing it.

However, while steganography is worth keeping in mind, I find myself skeptical of certain parts of this story:

1. In practice, RLHF makes the model's answers longer, not shorter. RLHF as done today seems to try to teach models to think step-by-step to improve accuracy. Almost everything out of GPT-4's mouth is overly long with many intermediate steps; I asked it "what's the maximum number of intersection points between a circle and a triangle", and it said 6 (correct) and followed it with a multi-part step-by-step style explanation, even though it already knew the right answer within the first few tokens. It's incredibly predisposed to thinking step-by-step and prefers long answers to short ones.
2. Will human evaluators in the future prefer short answers instead? That remains to be seen. The priority of firms like OpenAI is to have the models be accurate, so if short answers result in lower accuracy (as is surely the case), it's unlikely that OpenAI will train the LLM towards short answers. If OpenAI really really wanted short answers to math questions, they could do it via a 2-step process: first, an overly-verbose, step-by-step LLM like GPT-4; second, feed that output into a summarizing LLM which extract a 1-sentence summarization, and show the user that.
3. The approach in (2) will almost certainly lead to much higher accuracy on complex problems than training LLMs to give short outputs via RLHF (admittedly, this is at the cost of being somewhat slower).  Therefore, I find it unlikely that people will train LLMs via RLHF to give short answers. And without pressure towards short answers, there's no incentive to develop steganography.
4. Even if there is pressure towards short answers, I don't think steganography necessarily helps. For steganography to be feasible, the boilerplate part of the answer -- the part that goes "I am a large language model trained by OpenAI" -- will have to have enough entropy to store whatever it is you're encoding. Why wouldn't human evaluators penalize such high-entropy boilerplate? I suspect high-entropy boilerplate will look less desirable to human evaluators than thinking step-by-step explicitly, but I guess that remains to be seen.
5. Even if LLMs learn steganography from the internet, they'll likely speak the steganographic language worse than they speak English -- therefore, absent incentive otherwise, they might prefer think in English when thinking step-by-step.
6. If LLMs do learn steganography, it should be fairly trivial to train an LLM to translate from the steganographic language back to English -- after all, we're assuming these LLMs are extremely capable, and they'll speak both English and the steganographic language very well. They should be able to translate between them as easily as between English and French. Humans just need to be on the lookout for this, and what was previously uninterpretable steganography might quickly become interpretable.
7. The type of watermarking that Aaronson is proposing is unlikely to make steganography any more likely than otherwise. Aaronson is just proposing to change the pseudorandom number generator that is used to sample tokens from the output distribution. Even without adopting Aaronson's scheme, OpenAI still uses pseudorandom numbers instead of true random numbers when sampling tokens; why does it worry you if the PRNG encodes "OA model v.1234 user XYZ" instead of encoding "the random seed was 12345", which is what it's currently encoding? In both cases there is an encoding here, and in both cases it's not going to be broken anytime soon.

Still, I do agree that steganography is an interesting possibility and could definitely arise in powerful LLMs that are accidentally incentivized in this direction. It's something to watch out for, and interesting to think about.

I agree with everything you wrote here. Thank you for taking the time to write this down.

I think there are potential mitigations strategies like:

• Removing LLM-generated data from training sets (there might be good detectors, or you could only use old data) and not use RLHF; 
• Preventing models from using steganography enough that you could find provable and low bounds on how much data it can send itself. For example, you could temper with model generations (and pay the alignment tax associated with it);
• Finding steganography detection schemes (and maybe train against them?);
• Learning to decipher steganographic encoding (this feels really hard, but tractable since this is a game human can play by purposefully adding steganography to their models).

I think the above are *really hard*, but still easy compared to fully solving mechanistic interpretability / solving the alignment problem, which is why I'm excited about the research directions above.

Do you think there are reasons to expect all the strategies I mentioned to be harder than alternative alignment research directions?

The argument isn't "human = GPT, human use information X in chains of thought, therefore GPT use information X in chains of thought", which is what you seem to suggest.

I think I can make a version of this argument which doesn't rely on the human analogy at all:

1. You can't do advance deception and mesa optimization in <1k serial steps
2. The reason why chains of though works at all is not because GPTs put information there on purpose, but rather because humans put useful information there, GPTs imitate this, and then it can use information it has put there. It would be surprising if it encoded "by accident" information in a way humans can't understand
3. You can do human level AI research with <1k serial step per forward pass with chains of thought
4. Therefore, GPTs will be able to do human level AI research with GPTs not powerful enough to do hidden deception

Step 2 is not as crisp as I would like it, but it's definitely not an argument as weak as "GPT=humans".

A prompt including these two incorrect reasoning example can have better performance over some human engineered CoT prompts.

Yes. An even better example would be a different paper showing that you can simply shuffle all the answers in the few-shot prompt to improve over zero-shot. This was tweeted with the snark 'how is few-shot learning "meta-learning" if the answers don't need to be right?' But of course, there's no problem with that. All meta-learning is about is about solving the problem, not about respecting your preconceived notions of how the model 'ought' to compute. (Many optimal strategies, such as exact ones obtained by dynamic programming, can look bizarre to humans, so that's not a good criteria.)

This is because contrary to OP, a GPT model is not doing something as simple as reasoning explicitly through text or System I pattern-matching. (Or at least, if it is, the meaning of those two things are much broader and more opaque than one would think and so merely replaces an enigma with a mystery.) A GPT model doing few-shot learning is doing meta-learning: it is solving a (very large) family of related tasks using informative priors to efficiently and Bayes-optimally infer the latent variables of each specific problem (such as the agent involved, competence, spelling ability etc) in order to minimize predictive loss. Including example questions with the wrong answers is still useful if it helps narrow down the uncertainty and elicit the right final answer. There are many properties of any piece of text which go beyond merely being 'the right answer', such as the a priori range of numbers or the formatting of the answer or the average length or... These can be more useful than mere correctness. Just as in real life, when you see a confusing question and then see an incorrect answer, that can often be very enlightening as to the question asker's expectations & assumptions, and you then know how to answer it. ('Bad' prompts may also just stochastically happen to tickle a given model in a way that favors the 'right' family of tasks - think adversarial examples but in a more benign 'machine teaching' way.)

Prompts are not 'right' or 'wrong'; they are programs, which only have meaning with respect to the larger family of prompts/tasks which are the context in which they are interpreted by the neural computer. Since you still don't know what that computer is or how the prompt is being interpreted, you can't say that the baseline model is reasoning how you think it is based on a naive human-style reading of the prompt. It obviously is not reasoning like that! RLHF will make it worse, but the default GPT behavior given a prompt is already opaque and inscrutable and alien.

(If you don't like the meta-learning perspective, then I would point out the literature on LMs 'cheating' by finding various dataset biases and shortcuts to achieve high scores, often effectively solving tasks, while not learning the abilities that one assumed was necessary to solve those tasks. They look like they are reasoning, they get the right answer... and then it turns out you can, say, remove the input and still get high scores, or something like that.)

Thanks for your detailed feedback!

I don't see a strong conceptual difference between GPTs and models trained with in-fill objectives. In-fill objectives have some advantages for some tasks (like the tasks used to pretrain them), but they have the same properties when it comes to the number of sequential steps in one in-fill step vs multiple sequential in-fills. Like GPTs, they aren't easy to understand in one in-fill step, but I expect sequential in-fills to be close to human thoughts, though I haven't seen exotic examples of sequential in-fills which make use of the fact that you can fill text in any order. (Fill-in models are also harder to train at scale, right? Do some people still expect these kinds of model to play a role in the road to AGI?)

Instruction fine-tuning is fine-tuning a prompt across many questions, right? I've heard that they are often not human-understandable, which is not very reassuring. (The prompt might encode increasingly detailed reasons and ways to escape supervision, just as RL could hypothetically learn a hard-coded deception strategy? To be honest, I thought more about the power of GPTs at inference time than at training time, where the framing in terms of sequential steps breaks down.)

What do you mean by RLHF via expert distillation?

The big difference I see between NTP and RL is that in RL the model has opportunities to create "coordination with itself", which can take the form of hard-coded deception plans which don't require much thought, or steganography techniques (which is really challenging if you never had a chance to plan for it in advance, but really easy if you are trained with RL with wrong incentives).

At comparable levels of competence, I expect sampling BoN on a human imitator to be much safer than an RLHF'ed or expert distilled model, because there isn't this opportunity for the model to create "coordination with itself".

Same for prompt engineering, especially if your search process looks like "look by hand for things that would help humans" and not like "use gradient descent to find a good prompt", where you have way less control over the information that is used by the model (adversarial attacks that push the right knobs inside the model look might be the way to get the most performance, but it also means you don't know what these knobs are). Still looks better than pure RL since it only helps a little to create "coordination with itself".

But yeah, there is a big spectrum between pure NTP and pure RL, and I don't have enough conceptual clarity to draw a clear boundary.

I agree that it seems unlikely that GPTs in the near future will do zero shot stenography, but why do you expect that GPTs will fail to incorporate system 2 logic into its system 1? Is this mainly a competitiveness reason?

The core claim only holds for competitiveness reason. I would expect a "Dyson Sphere GPT" to be far beyond humans at everything using System 1 alone. But I still need the assumption that "things hard for human's System1 are hard for GPTs' System 1". This one is wrong for some task where GPTs' have ungodly amount of training (like "a vs the" or pure NTP), but I expect it to hold for all actually relevant task because the number of serial steps is of the same order of magnitude, and humans can roughly match GPTs width given enough training. For example, I expect that if some human try hard to solve multiple choice math tests using their intuition (for example, having to answer just after it has been read to them at 2x speed), they will crush GPTs until ~AGI.

Given that GPT is imitating humans who do deceive each other and themselves, including many who are much more sophisticated liars than a 5 year old + mind reader, I'm not sure why you're so confident that we'd get so much transparency by default.

I feel like mimicking liars doesn't teach you to lie well enough to fool humans on purpose. I might have been mistaken to bring up self-deception. Knowing how to do "simple lies" like "say A when you know that B" is quite useful in many parts of Webtext, but I can't think of kinds of text where you actually need to do "think in your lie" (i.e. put information in your lie you will use to think about your ulterior motives better). Humans don't have to do that because for complex lies, humans have the luxury of not having to say everything they are thinking about! Therefore, I would be surprised there was enough Webtext to teach you the kind of deception strategy which you need to pull off to plan within the prompt while being watched with models not strong enough to do human-level AI Research. Please tell me if you can think of a kind of text where lies where you "think through your lies" would be useful for NTP!

A prompt including these two incorrect reasoning example can have better performance over some human engineered CoT prompts. 

Thanks for these surprising CoT example!

I'm not sure how surprising it is that CoT examples with bad reasoning makes the model generate good CoT, nor how relevant it is. For example, I find it more troubling that prompt optimization finds incomprehensible prompts. But on the other hand, I have never seen models successfully generate absurd but useful CoT, and I expect they won't be able to because they haven't been trained to generate CoT useful to themselves. Therefore, I expect that the only way model can generate CoT useful to themselves is to generate CoT useful to humans (which they ~know how to do), and use the information humans find useful they have put in the CoT. I would be surprised if high likelihood CoT really useful to humans were also really useful to models for drastically different reasons.

(Note: figuring out the kind of reasoning would be useful is not that easy, even for humans, and I'm not sure teachers are great at figuring out how to best explain how to reason. I wouldn't be surprised if 7-graders had better performances with AQuA horrible examples than with some human engineered prompts.)

I feel like the different possible thoughts happen sequentially, I think about something wrong, then something right (or in general, I think sequentially about things, it's not always about wrong and right). Also, I would bet that if you could measure that under IRM, the broadcasting would happen on wrong, then on right.

1. sample wrong and right
2. choose
3. broadcast

Looks much more like a succession of sample and broadcast:

1. sample wrong
2. broadcast wrong
3. sample right (because your brain often samples multiple different things sequentially, especially if the first one doesn't "feel right")
4. broadcast right
5. sample "I recognize this second thought as right"
6. broadcast "I recognize this second thought as right" (which strengthened right and discards wrong, and prompts parts of the brain to use right for further thoughts)