All of Sam Marks's Comments + Replies

To be clear, I'm not classifying all uses of SFT as RL (for example, I would not call SFT on human expert demonstrations RL). It's specifically SFT on highly-rated model outputs -- i.e. having the model produce a bunch of rollouts, labeling them with rewards, training the model to imitate the top-rewarded rollouts, and repeating -- which I'm calling RL here. Note that this training process does aim the model towards high-reward, and is very similar to the online decision transformer, which is typically classed as an RL technique. 

So I still feel that ... (read more)

This is just a terminological difference: supervised fine-tuning on highly rated outputs is a type of RL. (At least according to how many people use the term.)

[edit: this says the same thing as Quintin's sibling comment]

Important context for those who don't know it: the main difference between text-davinci-002 and text-davinci-003 is that the latter was trained with PPO against a reward model, i.e. RLHF as laid out in the InstructGPT paper. (Source: OpenAI model index.)

In more detail, text-davinci-002 seems to have been trained via supervised fune-tuning on the model outputs which were rated highest by human reviewers (this is what the model index calls FeedME). The model index only says that text-davinci-003 wa... (read more)

If anyone is interested, here are LW users' scores (in orange) inside the distribution of all scores. 

(I've just realized the histogram in the applet is cutting off the two leftmost bars, so this histogram will look very slightly different than the one there until I fix this later tonight; this is the correct one. Fixed.)

Thanks, that's a good suggestion! I've done so.

These are all fantastic questions! I'll try to answer some of the ones I can. (Unfortunately a lot of the people who could answer the rest are pretty busy right now with EAGxBerkeley, getting set up for REMIX, etc., but I'm guessing that they'll start having a chance to answer some of these in the coming days.)

Regarding the research program, I'm guessing there's around 6-10 research projects ongoing, with between 1 and 3 students working on each; I'm guessing almost none of the participants have previous research experience. (Kuhan would have the actual nu... (read more)

Yep, sorry, I've probably been pretty unclear with describing this example.

I'm confused about why you think it both (a) not plan ahead of time to disempower humans, and (b) disempower humans when it has the chance. If the predictive model is accurate enough such that it is predictable that disempowering humans would be instrumentally useful, then wouldn't the model incorporate that into its earlier plans?

For the sake of simplicity, let's ignore the "execute on arbitrary short-term goals" half of the system I described, and just consider a system which was ... (read more)

I see two distinctions between a system like the one I described and a system with long-term goals in the usual sense. First, the goal "write down a plan which, if followed, would lead to long-term profit" is itself a short-term goal which could plausibly be trained up to human-level with a short-term objective function (by training on human-generated predictions). So I think this mechanism avoids the arguments made in claims 4 and 5 of the post for the implausibility of long-term goals (which is my motivation for mentioning it). (I can't tell if claim 6 w... (read more)

I think that a competent human actor assisted by short-term AI systems plausibly could take over the world this way; I'm just inclined to call that a misuse problem rather than an alignment problem. (Or in other words, fixing that requires solving the human alignment problem, which feels like it requires different solutions, e.g. coordination and governmental oversight, than the AI alignment problem.)

Thanks for writing this -- I found it interesting, thoughtful, and well-written.

One distinction which seems useful to make is between:

• long-term goals
• long-term planning
• long-term capabilities (i.e. the ability to reliably impact the long-term future in a particular way).

It seems to me that this post argues that:

1. AI systems' long-term planning won't be that much better than humans' (claims 1 and 3).
2. AI systems won't develop long-term goals (claims 4, 5, and 6).
3. Given (1) (and given that both humans and AI systems with long-term goals will have access to systems

Non-central nitpick:

As it turns out, transformers can do reinforcement learning in-context

This seems to just be vanilla in-context learning, rather than any sort of in-context RL. (Also I'm skeptical that the linked paper actually provides evidence of in-context RL in any nontrivial sense.)

This seems like a good way to think about some of the examples of mode collapse, but doesn't obviously cover all the cases. For example, when asking the model to produce a random number, is it really the case that there's a particular conversational goal which the RLHF'd model is optimizing, such that 97 is the best random number for that goal? In this case, Paul's guess that RLHF'd models tend to push probability mass onto the base model's most likely tokens seems more explanatory.

I agree that something like this would excellent. I unfortunately doubt that anything so cool will come out of this experiment. (The most important constraint is finding a HAIST member willing to take on the project of writing something like this up.)

If things go well, we are tentatively planning on sharing the list of core disagreements we identify (these will probably look like cruxes and subquestions) as well as maybe data about our members' distribution of views before and after the debate.

This recent comment thread discussing whether RLHF makes any progress beyond the classical "reward the agent when humans press the reward button" idea.

Thanks, that's a useful clarification; I'll edit it into the post.

In-context RL strikes me as a bit of a weird thing to do because of context window constraints. In more detail, in-context RL can only learn from experiences inside the context window (in this case, the last few episodes). This is enough to do well on extremely simple tasks, e.g. the tasks which appear in this paper, where even seeing one successful previous episode is enough to infer perfect play. But it's totally insufficient for more complicated tasks, e.g. tasks in large, stochastic environments. (Stochasticity especially seems like a problem, since yo... (read more)

Excellent work!

I had previously expected that training with KL-regularization would not be equivalent to early stopping in terms of its effects on RM overfitting, so I'm quite interested to see evidence otherwise. Two questions related to this:

1. In figure 9 of the paper (copied below), it looks like, from the perspective of the proxy RM, training with KL-regularization does not have the same effect as early stopping. This is pretty shocking to me: looking at only the gold RM lines of figure 9, you'd be tempted to guess that RL with KL penalty learns approxim

I really appreciated this comment for making the connection between this paper and IDA.

More explicitly, to the extent that you can think of the original large language model as simulating a human, there's an analogy between:

• asking the LLM to reason about its inputs and then training on the conclusion of the reasoning (what this paper does)
• asking simulated humans to reason about a problem and then training a new model on their conclusions (the basic idea behind iterated distillation and amplification).

This is a also a great chance for IDA skeptics to try to... (read more)

Note that if you want logits to work with, you could put a classification head on your LM and then train on the easy classification task where each input consists of a prompt, completion, and chain of thought. (In other words, you would have the LM reason about injuriousness using chain of thought as you did above, and afterwards feed the entire prompt + completion + chain of thought into your injuriousness classifier.)

This would let you backprop to tokens in the prompt + completion + chain of thought, and if you're willing to store the computational graph... (read more)

This seems very useful -- thanks for doing it!

Some paper suggestions:

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

There is mounting empirical evidence of emergent phenomena in the capabilities of deep learning methods as we scale up datasets, model sizes, and training times. While there are some accounts of how these resources modulate statistical capacity, far less is known about their effect on the computational problem of model training. This work conducts such an exploration through the lens of learning k-sparse par

But you still need online access to our MDP (i.e. reward function and transition function), don't you?

Yep, that's right! This was what I meant by "the agent starts acting in its environment" in the description of an ODT. So to be clear, during each timestep in the online phase, the ODT looks at a partial trajectory

$g_1,o_1,a_1,\dots ,g_{t-1},o_{t-1},a_{t-1},g_t,o_t$

of rewards-to-go, observation, and actions; then selects an action $a_t$ conditional on this partial trajectory; and then the environment provides a new reward $r_t$ (so that $g_{t+1}=g_t-r_t$) and obser... (read more)

(separate comment to make a separate, possibly derailing, point)

> If the answer were yes, then this policy would do a really bad job predicting 499 of the 500 past episodes with top 5% reward, so I conclude the answer probably isn't yes.

For safety, 'probably' isn't much of a property.

I mostly view this as a rhetorical flourish, but I'll try to respond to (what I perceive as) the substance.

The "probably" in my sentence was mainly meant to indicate out-of-model uncertainty (in the sense of "I have a proof that X, so probably X" which is distinct from "I h... (read more)

I continue to think you're wrong here, and that our disagreement on this point is due to you misunderstanding how an ODT works.

Your simple DT is not keeping an episodic buffer around to do planning over or something, it's just doing gradient updates. It doesn't "know" what the exact empirical distribution of the last 10,000 episodes trained on was nor would it care if it did

To be clear: an ODT does keep an episodic buffer of previous trajectories (or at least, that is the implementation of an ODT that I'm considering, which comports with an ODT as implemen... (read more)

But it will still have the problems of modeling off-distribution poorly, and going off-distribution.

Yep, I agree that distributional shift is still an issue here (see counterpoint 1 at the end of the "Safety advantages" section).

---

> Novel behaviors may take a long time to become common [...]

I disagree. This isn't a model-free or policy model which needs to experience a transition many times before the high reward can begin to slowly bootstrap back through value estimates or overcome high variance updates to finally change behavior, it's a model-based R

Bug report: the "Some remarks" section of this post has a nested enumerated list. When I open the post in the editor, it displays as 

1. [text]

> a. [text]

> b. [text]

> c. [text]

2. [text]

(where the >'s represent indentation). But the published version of the post displays this as

1. [text]

> 1. [text]

> 2. [text]

> 3. [text]

2. [text]

This isn't a huge deal, but it's a bit annoying since I later refer to the things I say in the nested list as e.g. "remark 1(c)." 

I like this idea! And these are excellent first considerations on what exactly the surgeon should look like.

It seems to me that bounding the size of the modification the surgeon can make to any one activation has some issues. For instance, suppose that we're trying to apply this scheme to the agent which defects if it sees a factorization of RSA-2048. A plausible way this agent could work internally is that there is a particular activation which tracks whether the agent has seen a factorization of RSA-2048: the activation is very large whenever the agent h... (read more)

Why privately?!

(Treating this as non-rhetorical, and making an effort here to say my true reasons rather than reasons which I endorse or which make me look good...)

In order of importance, starting from the most important:

1. It would take a lot of effort to turn the list of disagreements I wrote for myself into a proper post, and I decided the effort wasn't worth it. I'm impressed how quickly Paul wrote this response, and it wouldn't surprise me if there are some people reading this who are now wondering if they should still post their rebuttals they've been d

(I mostly endorse this explanation, but am also writing a reply with some more details.)

I've also been perplexed by the focus on Tao in particular. In fact, I've long thought that if it's a good idea to recruit a top mathematician to alignment, then Peter Scholze would be a better choice since

1. he's probably the greatest active mathematician
2. he's built his career out of paradigmatizing pre-paradigmatic areas of math
3. he has an interest in computer proof-checking.

That said, I'm quite confident that Scholze is too busy revolutionizing everything he touches in mathematics to be interested in switching to alignment, so this is all moot.

(Also, I recogn... (read more)

I wish I could say that there was some sort of hilarious self-referential joke here, but actually I'm just bad at counting, oops. At this point I probably won't fix it for fear of ruining in-text section references. 

(I should clarify that I'm not an expert. In fact, you might even call me "an amateur who's just learning about this stuff myself"! That said...)

RLHF attempts to infer a reward function from human comparisons of task completions.

I believe that RLHF more broadly refers to learning reward models via supervised learning, not just the special case where the labelled data is pairwise comparisons of task completions. So, for example, I think that RLHF would include e.g. learning a reward model for text summaries based on scalar 1-10 feedback from humans, rather ... (read more)

This was a nice post! I appreciate the effort you're making to get your inside view out there.

A correction:

The ultimate goal is to get a reward model that represents human preferences for how a task should be done: this is also known as Inverse Reinforcement Learning.

Based on this sentence, you might be conflating value learning (the broad class of approaches to outer alignment that involve learning reward models) with IRL, which is the particular sub-type of value learning in which the ML model tries to infer a reward function by observing the behavi... (read more)

Relevant: Mark Xu's recent (short) EA forum post 'Dropping out isn't a plan.

1. Yeah, I agree this was an issue. I've changed the title and made an edit to reflect the focus on outer alignment.
2. I feel pretty resistant to changing anything about this. I feel like LW should be a place where, when you write "if X then Y," people should know not to infer "X is true" or "I'm going to argue for X." Moreover, one point I was trying to get across is that instead of thinking in terms of whether "ELK works" or not, you can think in terms of how many questions our ELK techniques allow us to elicit honest answers for; the larger that collection of

Thanks, this is indeed a point I hadn't fully appreciated: even if a reward function generalizes well OOD, that doesn't mean that a policy trained on that reward function does.

It seems like the issue here is that it's a bad idea to ever take your policy offline, analogously to what happens in reward modeling from human feedback (namely, reward models stops being good once you take them offline). Does that seem right? Of course, keeping an RL agent in learning mode forever might also have issues, most obviously unsafe exploration. Are there other things that also go wrong?

Thanks, I should have clarified that everywhere I say "alignment" in this post, I'm really talking about (outer) intent alignment, which of course excludes a whole barrage of safety-relevant concerns: safe exploration, robustness to distributional shift, mesa-optimizers, etc.

That said, I think the particular concern expressed in the paper you link -- namely, that the agent's reward model could break OOD while the agent's capabilities remain otherwise intact -- doesn't seem like it would be an issue here? Indeed, the agent's reward model is pulled out of it... (read more)

I agree that the term "deception" conflates "deceptive behavior due to outer alignment failure" and "deceptive behavior due to inner alignment failure" and that this can be confusing! In fact, I made this same distinction recently in a thread discussing deceptive behavior from models trained via RL from human feedback.

The idea that conditioning on unCLIP-produced image vectors instead of text vectors would improve diversity seems very bewildering. And I really have a hard time swallowing the explanation "maybe this happens because for a given CLIP image vector v, there's a large equivalence class of images that all approximately encode to v." After all, this explanation doesn't actually have anything to do with conditioning on image vs. text vectors; in other words, whether we condition on image or text vectors, the final resulting image should still have a large equiva... (read more)

Ah cool, I see -- your concern is that maybe RLHF is perhaps better left to the capabilities people, freeing up AI safety researchers to work on more neglected approaches. 

That seems right to me, and I agree with it as a general heuristic! Some caveats:

1. I'm random person who's been learning a lot about this stuff lately, definitely not an active researcher. So my opinions about heuristics for what to work on probably aren't worth much.
2. If you think RLHF research could be very impactful for alignment, that could make up for it being less neglected than o

I'll be performing a (modest) update on the results of this experiment, and I strongly endorse John's comment here as an explanation of why -- it's testing a worldview that's upstream of both this AC debate and alignment. 

In my case, the worldview being tested isn't about civilizational inadequacy. Rather, it's about how likely optimizers (e.g. the market, an AI system) are to do things that seem to satisfy our preferences (but actually have hidden bad side effects that we're not smart enough to notice) vs. do things that actually satisfy our preferen... (read more)

What would you say is the main benefit from the RL from Human Feedback research so far? What would have happened if the authors had instead worked on a different project?

I feel like these questions are a little tricky to answer, so instead I'll attempt to answer the questions "What is the case for RL from human feedback (RLFHF) helping with alignment?" and "What have we learned from RLFHF research so far?"

What is the case for RLFHF helping with alignment?

(The answer will mainly be me repeating the stuff I said in my OP, but at more length.)

The most naive c... (read more)