I never get these discussions. It's only ever online that I see complaints. Almost everyone reclines on long flights. It's normal. It's expected. If it makes you uncomfortable that's a you problem, everyone else seems fine with it. If it makes you physically uncomfortable, pay for extra leg room. Don't make your problem the problem of another passenger.

Nor I. TFA is about a chess bot, yet here we are discussing seat reclining etiquette.

At least we aren’t discussing ad blockers for the 18th time this week.

That's a skewed conclusion you're drawing. Are you really surprised that people aren't willing to risk escalating the situation on a plane, arguing with what's likely the very inconsiderate person in front of them? Most people have an aversion to conflict. It doesn't mean "they're fine with it". You probably don’t advertise in real life how much you lean back and not care who’s behind you out of fear that people will change your opinion of you. Real life is a harsh mistress.

I've bumped into people and they said "sorry", do you think they wanted me to bump into them, liked it, and actually believed it was their mistake? No, I just tower at close to 2m so they didn't want to escalate the situation.

P.S. I always look at who sits behind me, if they're "space constrained" or not, and almost always ask if I can recline. Sometimes I don't bother, clearly the person will suffer. Sometimes they said "I'd rather not, thank you". Many times they said "fine". I used to fly a lot and my experience was very clearly not that "everyone is fine". I was never fine even if I didn't start arguing. So how would you have known?

I've discussed this with various people IRL. No one, including taller people than me, ever complained about people being inconsiderate for reclining. Every tall person complains about leg room.

The vast majority of people do not think it's inconsiderate to recline. They think it's normal and that the function is there for a reason.

I actually think it's inconsiderate to complain to the person in front if they want to recline. The only time that is acceptable is when meals are served.

Greenfield is fundamentally easier than maintaining existing software. Once software exists, users expect it to behave a certain way and they expect their data to remain usable in new versions.

The existing software now imposes all sorts of contraints that may not be explicit in the spec. Some of these constraints end up making some changes very hard. Bad assumptions in data modeling can make migrations a nightmare.

You can't just write entirely new software every time the requirements change.

1) Making the application small enough, and breaking it apart if needed (e.g. I've refactored my old 'big' app into 10 micro-apps).

2) Selecting an architecture that will work, looking after the data modelling and architecture yourself rather than delegating this to the LLM (it can implement it - but you need to design it).

3) Trusting that the LLM is capable enough to implement new requirements or fixes as required.

If requirements change so substantially that it's not possible, you can write new software as requirements change - as per point 1, you will have made your application modular enough that this isn't a significant concern.

Just because it's ignored every time doesn't make this time okay.

I don't believe they can. LLMs have no concept of truth.

What's likely is that the "truth" for many subjects is represented way more than fiction and when there is objective truth it's consistently represented in similar way. On the other hand there are many variations of "fiction" for the same subject.

So think of it like this, to create the model we use terabytes of data. Then we do RL which is probably less than one percent of additional data involved in the initial training.

The change in the model is that reliability is increased and hallucinations are reduced at a far greater rate than one percent. So much so that modern models can be used for agentic tasks.

How can less than one percent of reinforcement training get the model to tell the truth greater than one percent of the time?

The answer is obvious. It ALREADY knew the truth. There’s no other logical way to explain this. The LLM in its original state just predicts text but it doesn’t care about truth or the kind of answer you want. With a little bit of reinforcement it suddenly does much better.

It’s not a perfect process and reinforcement learning often causes the model to be deceptive an not necessarily tell the truth but it more gives an answer that may seem like the truth or an answer that the trainer wants to hear. In general though we can measurably see a difference in truthfulness and reliability to an extent far greater than the data involved in training and that is logical proof it knows the difference.

Additionally while I say it knows the truth already this is likely more of a blurry line. Even humans don’t fully know the truth so my claim here is that an LLM knows the truth to a certain extent. It can be wildly off for certain things but in general it knows and this “knowing” has to be coaxed out of the model through RL.

Keep in mind the LLM is just auto trained on reams and reams of data. That training is massive. Reinforcement training is done on a human basis. A human must rate the answers so it is significantly less.

I can think of several offhand.

1. The effect was never real, you've just convinced yourself it is because you want it to be, ie you Clever Hans'd yourself.

2. The effect is an artifact of how you measure "truth" and disappears outside that context ("It can be wildly off for certain things")

3. The effect was completely fabricated and is the result of fraud.

If you want to convince me that "I threatened a statistical model with a stick and it somehow got more accurate, therefore it's both intelligent and lying" is true, I need a lot less breathless overcredulity and a lot more "I have actively tried to disprove this result, here's what I found"

First, the claim that RLHF materially reduces hallucinations and increases factual accuracy is not anecdotal. It shows up quantitatively in benchmarks designed to measure this exact thing, such as TruthfulQA, Natural Questions, and fact verification datasets like FEVER. Base models and RL-tuned models share the same architecture and almost identical weights, yet the RL-tuned versions score substantially higher. These benchmarks are external to the reward model and can be run independently.

Second, the reinforcement signal itself does not contain factual information. This is a property of how RLHF works. Human raters provide preference comparisons or scores, and the reward model outputs a single scalar. There are no facts, explanations, or world models being injected. From an information perspective, this signal has extremely low bandwidth compared to pretraining.

Third, the scale difference is documented by every group that has published training details. Pretraining consumes trillions of tokens. RLHF uses on the order of tens or hundreds of thousands of human judgments. Even generous estimates put it well under one percent of the total training signal. This is not controversial.

Fourth, the improvement generalizes beyond the reward distribution. RL-tuned models perform better on prompts, domains, and benchmarks that were not part of the preference data and are evaluated automatically rather than by humans. If this were a Clever Hans effect or evaluator bias, performance would collapse when the reward model is not in the loop. It does not.

Fifth, the gains are not confined to a single definition of “truth.” They appear simultaneously in question answering accuracy, contradiction detection, multi-step reasoning, tool use success, and agent task completion rates. These are different evaluation mechanisms. The only common factor is that the model must internally distinguish correct from incorrect world states.

Finally, reinforcement learning cannot plausibly inject new factual structure at scale. This follows from gradient dynamics. RLHF biases which internal activations are favored, it does not have the capacity to encode millions of correlated facts about the world when the signal itself contains none of that information. This is why the literature consistently frames RLHF as behavior shaping or alignment, not knowledge acquisition.

Given those facts, the conclusion is not rhetorical. If a tiny, low-bandwidth, non-factual signal produces large, general improvements in factual reliability, then the information enabling those improvements must already exist in the pretrained model. Reinforcement learning is selecting among latent representations, not creating them.

You can object to calling this “knowing the truth,” but that’s a semantic move, not a substantive one. A system that internally represents distinctions that reliably track true versus false statements across domains, and can be biased to express those distinctions more consistently, functionally encodes truth.

Your three alternatives don’t survive contact with this. Clever Hans fails because the effect generalizes. Measurement artifact fails because multiple independent metrics move together. Fraud fails because these results are reproduced across competing labs, companies, and open-source implementations.

If you think this is still wrong, the next step isn’t skepticism in the abstract. It’s to name a concrete alternative mechanism that is compatible with the documented training process and observed generalization. Without that, the position you’re defending isn’t cautious, it’s incoherent.

He doesn't care. You might as well be arguing with a Scientologist.

If EU countries commit to a conflict, Russia has no chance. It makes nuclear escalation a real risk though.

That's not the case at all though. The LLM doesn't have a mental model of what the expected final result is, so how could it possibly verify that?

It has a description in text format of what the engineer thinks he wants. The text format is inherently limited and lossy and the engineer is unlikely to be perfect at expressing his expectations in any case.

Nah, it's not noise. It's a useful reminder not to take any comments too seriously and that this topic is far outside the average commenter's expertise.

Yes, it factually is, because...

> It's a useful reminder not to take any comments too seriously

...this is factually incorrect, because GP comment is literally not saying that - it's a specific dunk on a specific subset of critical comments with zero useful information about which comments or bad or why they're bad or any evidence to back up the assertion that they're bad or anything else useful.

(GP did go back and respond to some other comments with specific technical criticisms - after they made this initial comment. The initial comment itself is still highly problematic, as are fallacious praise of it, like this one.)

I say this as a psychologist who is advising you to ignore all claims to the contrary, because they are misinformed. It is clear from the literature.

And then you stopped using their service right?

Other times they turn a blind eye and choose to provide the service (and collect my money) despite the lack of agreement to some part of their standard terms and their tacit acknowledgement that I didn't accept them. On two occasions their legal team responded and said "that's fine", and once they actually fixed their ToS.

People who didn't grow up dealing with paper contracts where you could easily redline and send back for countersigning don't seem to understand that you don't just need to blindly say "yes" to everything a company tries to foist upon you.

The price of drugs that make it to market needs to not only cover the cost to produce the drug, but also the cost of R&D and the cost of R&D of all the drugs that fail to get to market.

Now this gets complicated when a company sells in different markets with actors that have different negotiating power. It makes sense to sell in any market where the company can get a profit per unit sold without including R&D. But if none of the markets allow enough profit to cover R&D, then it's not really worth developing any new drugs at all anymore.

That's why people say that the US is basically subsidizing drug development. It's not that it's not profitable to sell in the rest of the world, it's just that margins are much lower which allows for a lot less risk-taking on R&D.

It absolutely is a disability! The fact that it's easy to deal with it doesn't change that fact.

I would not find it credible that it has a real impact on education though.