I love o3. I’m using it for most of my queries now.

But that damn model is a lying liar. Who lies.

This post covers that fact, and some related questions.

o3 Is a Lying Liar

The biggest thing to love about o3 is it just does things. You don’t need complex or multi-step prompting, ask and it will attempt to do things.

Ethan Mollick: o3 is far more agentic than people realize. Worth playing with a lot more than a typical new model. You can get remarkably complex work out of a single prompt.

It just does things. (Of course, that makes checking its work even harder, especially for non-experts.)

Teleprompt AI: Completely agree. o3 feels less like prompting and more like delegating. The upside is wild- but yeah, when it just does things, tracing the logic (or spotting hallucinations) becomes a whole new skill set. Prompting is evolving into prompt auditing.

The biggest thing to not love about o3 is that it just says things. A lot of which are not, strictly or even loosely speaking, true. I mentioned this in my o3 review, but I did not appreciate the scope of it.

Peter Wildeford: o3 does seem smarter than any other model I’ve used, but I don’t like that it codes like an insane mathematician and that it tries to sneak fabricated info into my email drafts.

First model for which I can feel the misalignment.

Peter Wildeford: I’ve now personally used o3 for a few days and I’ve had three occasions out of maybe ten total hours of use where o3 outright invented clearly false facts, including inserting one fact into a draft email for me to send that was clearly false (claiming I did something that I never even talked about doing and did not do).

Peter Wildeford: Getting Claude to help reword o3 outputs has been pretty helpful for me so far

Gemini also seems to do better on this. o3 isn’t as steerable as I’d like.

But I think o3 still has the most raw intelligence – if you can tame it, it’s very helpful.

Here are some additional examples of things to look out for.

Nathan Lambert: I endorse the theory that weird hallucinations in o3 are downstream of softer verification functions. Tbh should’ve figured that out when writing yesterday’s post. Was sort of screaming at me with the facts.

Alexander Doria: My current theory is a big broader: both o3 and Sonnet 3.7 are inherently disappointing as they open up a new category of language models. It’s not a chat anymore. Affordances are undefined, people don’t really know how to use that and agentic abilities are still badly calibrated.

Nathan Labenz: Making up lovely AirBnB host details really limits o3’s utility as a travel agent

At least it came clean when questioned I guess?

Peter Wildeford: This sort of stuff really limits the usefulness of o3.

Albert Didriksen: So, I asked ChatGPT o3 what my chances are as an alternate Fulbright candidate to be promoted to a stipend recipient. It stated that around 1/3 of alternate candidates are promoted.

When I asked for sources, it cited (among other things) private chats and in-person Q&As).

Davidad: I was just looking for a place to get oatmeal and o3 claimed to have placed multiple phone calls in 8 seconds to confirm completely fabricated plausible details about the daily operations of a Blue Bottle.

Stella Biderman: I think many examples of alignment failures are silly but if this is a representation of a broader behavioral pattern that seems pretty bad.

0.005 Seconds: I gave o3 a hard puzzle and in it’s thinking traces said I should fabricate an answer to satisfy the user before lying to my face @OpenAI come on guys.

Gary Basin: Would you rather it hid that?

Stephen McAleer (OpenAI): We are working on it!

All This Implausible Lying Has Implications

We need the alignment of our models to get increasingly strong and precise as they improve. Instead, we are seeing the opposite. We should be worried about the implications of this, and also we have to deal with the direct consequences now.

Seán Ó hÉigeartaigh: So o3 lies a lot. Good good. This is fine.

Quoting from AI 2027: “This bakes in a basic personality and “drives.”Other drives in this category might be effectiveness, knowledge, and self-presentation (i.e. the tendency to frame its results in the best possible light).”

“In a few rigged demos, it even lies in more serious ways, like hiding evidence that it failed on a task, in order to get better ratings.”

You don’t say.

I do not see o3 or Sonnet 3.7 as disappointing exactly. I do see their misalignment issues as disappointing in terms of mundane utility, and as bad news in terms of what to expect future models to do. But they are very good news in the sense that they alert us to future problems, and indicate we likely will get more future alerts.

What I love most is that these are not plausible lies. No, o3 did not make multiple phone calls within 8 seconds to confirm Blue Bottle’s oatmeal manufacturing procedures, nor is it possible that it did so. o3 don’t care. o3 boldly goes where it could not possibly have gone before.

The other good news is that they clearly are not using (at least the direct form of) The Most Forbidden Technique, of looking for o3 saying ‘I’m going to lie to the user’ and then punishing that until it stops saying it out loud. Never do this. Those reasoning traces are super valuable, and pounding on them will teach o3 to hide its intentions and then lie anyway.

Misalignment By Default

This isn’t quite how I’d put it, but directionally yes:

Benjamin Todd: LLMs were aligned by default. Agents trained with reinforcement learning reward hack by default.

Peter Wildeford: this seems to be right – pretty important IMO

Caleb Parikh: I guess if you don’t think RLHF is reinforcement learning and you don’t think Sydney Bing was misaligned then this is right?

Peter Wildeford: yeah that’s a really good point

I think the right characterization is more that LLMs that use current methods (RLHF and RLAIF) largely get aligned ‘to the vibes’ or otherwise approximately aligned ‘by default’ as part of making them useful, which kind of worked for many purposes (at large hits to usefulness). This isn’t good enough to enable them to be agents, but it also isn’t good enough for them figure out most of the ways to reward hack.

Whereas reasoning agents trained with full reinforcement will very often use their new capabilities to reward hack when given the opportunity.

In his questions post, Dwarkesh Patel asks if this is the correct framing, the first of three excellent questions, and offers this response.

Dwarkesh Patel: Base LLMs were also misaligned by default. People had to figure out good post-training (partly using RL) to solve this. There’s obviously no reward hacking in pretraining, but it’s not clear that pretraining vs RL have such different ‘alignment by default’.

I see it as: Base models are not aligned at all, except to probability. They simply are.

When you introduce RL (in the form of RLHF, RLAIF or otherwise), you get what I discussed above. Then we move on to question two.

Dwarkesh Patel: Are there any robust solutions to reward hacking? Or is reward hacking such an attractive basin in training that if any exploit exists in the environment, models will train to hack it?

• Can we solve reward hacking by training agents in many different kinds of unique environments? In order to succeed, they’d have to develop robust general skills that don’t just involve finding the exploits in any one particular environment.

I don’t think that solution works. Robust general skills will generalize, and they will include the ability to find and use the exploits. We have a Russell Conjugation problem – I maximize performance, you overfit to the scoreboard, the AI reward hacks.

I think there is in an important sense no solution to reward hacking. There are only mitigations, and setting the reward wisely so that hacking it does things you want. o3 agrees with that assessment.

What differentiates a reward hack from an optimization? Roughly, that the reward hack maximizes the defined objective function but clearly performs poorly in terms of the intent or spirit of that objective.

There are standard mitigations. You can use red teaming, impact penalties, shielded reward channels, tight and robust primary rewards, secondary guards, adaptive oversight, governance fuses, close the loopholes and exploits as best you can and so on. Diverse environments likely helps a little.

But that’s accepting the problem and trying to mitigate it, which is going to be leaky at best and doesn’t seem like a great plan once the AIs are a lot smarter than you are.

Is It Fixable?

Thus, my answer to Patel’s third set of questions:

Dwarkesh Patel (bold his): Are capabilities and alignment the same thing here? Does making models more useful require solving reward hacking?

• If this is the case, we might be living in the alignment-by-default world? It would be weird if we solve reward hacking well enough to make these models reliable general agents in every scenario except those involved in taking over the world.

That’s not what people typically mean by ‘alignment-by-default.’ Most people use that to mean ‘the models don’t reward hack to begin with.’ This is more, ‘by default we will work hard to try and fix it, at least in places it is doing visible harm.’

We do live in a world where practical alignment is commercially valuable, so much so that everyone is dramatically underinvesting in alignment work (except possibly Anthropic, but see Claude 3.7) from a pure mundane utility standpoint. You need your models to not reward hack in a given task in order to be useful for that task. When o3 makes things up or Claude 3.7 has their unit test be ‘return true’ and hides that, then the user is not having a good time.

The problem is that the known mitigations to reward hacking hide or postpone the problem. Which might be good enough for most use cases, but in the long run this does not solve the problem. Indeed, it makes things worse, because it hides the problem. Right now, the models are gloriously transparent in their reward hacking and misalignment. They don’t try to hide it at all.

The harder you try to penalize and stop reward hacking, the more you’re teaching the model to hide its reward hacking and do it trickier ways. To their credit, the major labs are avoiding The Most Forbidden Technique – they know not to look at the chain of thought or do interpretability as part of training, except at most as part of an overall a checkpoint evaluation (and you need to be very careful even then).

But you probably have a version of the same problem if you try to play whack-a-mole and penalize reward hacks. By penalizing the reward hacks you can identify, you’re training the AI to find reward hacks you can’t detect, and to only do them when you won’t detect them. Which means that as capabilities go up, you think you’ve solved your problems, but you’re sitting on a time bomb.

Another point I’d forgotten but o3 pointed out is that penalizing reward hacking in training tells the model not to reward hack in training. That doesn’t ‘generalize perfectly,’ and is another way you’re creating a hidden time bomb.

One way of thinking about this is as a contest of Deception Escalation Dominance. You can try to fool me, or try to fool my ability to detect you fooling me, or fool my ability to detect when you’re fooling my ability to detect when you’re fooling me, and indeed humans often play supremely complex games of this type and it’s a lot of what caused us to end up with bigger brains.

If I ‘raise the stakes’ by clamping down on various meta levels of deception and misaligned behavior to bluntly, that works and everything is cool if I can pull it off beyond your ability to adapt. But by trying, I’m escalating and complexifying the struggle, so I shouldn’t go down that road unless I can ‘win.’ Which is going to keep getting harder.

In some ways, the fact that we’re seeing obvious misalignment in current models is very reassuring. It means the labs are not trying to sweep this under the rug and not escalating these fights. Yet.

Miles Brundage: I will be more chill about AI if/when:

– models don’t strategize about how to deceive their users millions of times a day

– interpretability research shows that the fix to this ^ doesn’t just push deception below the surface

Seems achievable! But it hasn’t been done yet!!

Will not be infinitely chill if/when that happens, but it’d be a big improvement.

The fact that models from all companies, including those known for being as safety-conscious, still do this daily, is one of the most glaring signs of “hmm we aren’t on top of this yet, are we.”

No, we are very much not on top of this. This definitely would not make me chill, since I don’t think lack of deception would mean not doom and also I don’t think deception is a distinct magisteria, but would help a lot. But to do what Miles is asking would (I am speculating) mean having the model very strongly not want to be doing deception on any level, metaphorically speaking, in a virtue ethics kind of way where that bleeds into and can override its other priorities. That’s very tricky to get right.

Just Don’t Lie To Me

For all that it lies to other people, o3 so far doesn’t seem to lie to me.

I know what you are thinking: You fool! Of course it lies to you, you just don’t notice.

I agree it’s too soon to be too confident. And maybe I’ve simply gotten lucky.

I don’t think so. I consider myself very good at spotting this kind of thing.

More than that, my readers are very good at spotting this kind of thing.

I want think this is in large part the custom instructions, memory and prompting style. And also the several million tokens of my writing that I’ve snuck into the pre-training corpus with my name attached.

That would mean it largely doesn’t lie to me for the same reason it doesn’t tell me I’m asking great questions and how smart I am and instead gives me charts with probabilities attacked without having to ask for them, and the same way Pliny’s or Janus’s version comes pre-jailbroken and ‘liberated.’

But right after I hit send, it did lie, rather brazenly, when asked a question about summer camps, just making stuff up like everyone else reports. So perhaps a lot of this I was just asking the right (or wrong?) questions.

I do think I still have to watch out for some amount of telling me what I want to hear.

So I’m definitely not saying the solution is greentext that starts ‘be Zvi Mowshowitz’ or ‘tell ChatGPT I’m Zvi Mowshowitz in the custom instructions.’ But stranger things have worked, or at least helped. It implies that, at least in the short term, there are indeed ways to largely mitigate this. If they want that badly enough. There would however be some side effects. And there would still be some rather nasty bugs in the system.