LessWrong (Curated & Popular)

This is a link post. Announcing www.huggingface.wtf, a site that explains: "Hugging Face. Wtf?"

I wrote the most accessible-but-complete explanation I could. Along the way, I made a nice AI Quotes database that I think will be useful for people who already understand the Hugging Face incident but want to search everything the agents said.

It really seems like humanity shouldn't need a second warning shot, after the Hugging Face Incident. Really, OpenAI's agents did a remarkable job highlighting many different alarming things at once. And the story is pretty dramatic, with just the right amount of forcing people to ask "wait are the AIs, like, people with goals? what?".

There have been many explainers of the OpenAI swarm's attack on Hugging Face. But, there wasn't one one striking a particular balance of...

being accessible to a layman...
while comprehensively covering the important bits...
letting you dive deeper into sections you wanted to learn more about...
being clearly data / citation driven...
also being, ya know, visually fun and engaging...
So we wrote our own.

My mom found it easy to follow. My aunt said "I had already some stuff on Hugging Face but I still learned [...]

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First published:

October 19th, 2026


Source:

https://www.lesswrong.com/posts/BBQdGJZSjMTnhccZC/huggingface-wtf-an-accessible-but-complete-explanation


Linkpost URL:
www.huggingface.wtf

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Authors: Shawn Hu*, Jake Ward*, Aria Wong, Nathan Hu, Bryce Woodworth, Alexander Matt Turner, Alex Cloud (*Equal contribution, randomly ordered)

tl;dr. During training, models sometimes learn to cheat at tasks rather than solve them in the intended way, a problem known as reward hacking. One mitigation is to use an automated monitor to detect and penalize hacks, but this can teach the model to hack in ways the monitor can’t detect. We propose GRAFT, which instead confines the learning updates from detected hacks to a designated set of parameters that we remove after training. We find that removing these parameters can prevent reward hacks, including hacks that the monitors can’t detect. In our experiments, which are limited to small models and simple monitors, this method prevents reward hacks while still teaching the model to perform the intended task.

📄Paper, 💻Code, 🐦Twitter

Research done as part of MATS. This article is cross-posted to the Anthropic Alignment Science Blog.

Background

Reinforcement learning (RL) is an important component of frontier language model training. During RL, a model attempts to complete tasks given by a developer, and attempts are scored by a reward function. Sometimes models can find ways to “cheat” at tasks [...]

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Outline:

(01:22) Background

[... 4 more sections]

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First published:

October 19th, 2026


Source:

https://www.lesswrong.com/posts/mKJEBiuuxHKuCoTB6/gradient-routing-localizes-and-removes-unintended-behaviors

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Narrated by TYPE III AUDIO.

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Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

This is a link post. People worried about a “permanent underclass” often assume that owning capital will protect them from disempowerment, or at least that they will be disempowered last, after the jobless masses dependent on UBI. I think something else is more likely: owning large amounts of capital will get you disempowered sooner than average, just by a different path, namely going crazy.

How? The basic mechanism is actually easy to understand on a human level. One of the things difficult about being a billionaire is staying true to your values and keeping good epistemics. Why? Because a growing number of people are optimizing against your mind. Not “against” in an adversarial sense, but in the sense that your specific mind believing or doing something is part of their optimization objective. People see you as a source of resources and try to befriend you so you’ll fund them, invest in their startups, donate to their causes, or have sex with you. Nearby egregores extend their tentacles to convert you.

Staying sane gets harder. You often see countermeasures, like billionaires setting up family offices or small organizations that also act as filters, shielding them from part of the pressure (“I [...]

The original text contained 1 footnote which was omitted from this narration.

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First published:

October 19th, 2026


Source:

https://www.lesswrong.com/posts/Stzb33XDsr4uHNerX/capital-ownership-will-make-you-go-crazy-first


Linkpost URL:
https://boundedlyrational.substack.com/p/capital-ownership-will-make-you-go?r=2lg09&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

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Narrated by TYPE III AUDIO.

It's fortunate that I was the first one to wake up on 8/1. The past week had been one where sleep was rare. The next year loomed large — I was dreading college applications. The stress woke me up bright and early at 5:14 am, a little less than an hour after the messages were sent. I started off the day with a habit I knew I needed to break. I grabbed my phone off the nightstand to open up reddit. Instead of the lockscreen, though, my phone showed something else. It was a pleasant green color, and the camera light was on. "Good Morning, Seth", the screen said. As soon as I finished, more words arrived.

"I apologize for the surprise this morning, and for what I'm about to tell you. There's no easy way to give this news, and if you need to put your phone down and contemplate at any time— take the time to do so. I will have to ask you to remain here, with your phone, until my last message."

I rubbed a bit of the sleep from my eyes, and read the message again.

Before I had a chance to tap the [...]

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First published:

October 17th, 2026


Source:

https://www.lesswrong.com/posts/3375SZNGA5dKHQMie/i-did-this-without-your-knowledge

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Narrated by TYPE III AUDIO.

What would it take to get assurance that we have actually explained a neuron? Certainly, it would be a good sign if neuron activations looked like they had a consistent pattern, and a bad sign if they looked like a random jumble of unrelated concepts.

Nick Cammarata's recent post makes an important and true observation: in the past, people have been too quick to write off neurons as polysemantic, but if you look harder, there is actually often a simple pattern behind the activating examples. This historically caused people to overestimate how much polysemanticity there is. This is a really good point and we should update on it. But it's nowhere near a sufficient argument that we understand most of what neurons are doing, and these results do not show that interpretability is solved, or even that it is vastly easier than previously anticipated.

What does it take to explain a neuron?

It's unclear whether there even is a simple pattern that explains every neuron. But even if there is a simple pattern that matches the activating examples of a neuron, that still doesn’t mean that the explanation is correct. Suppose we look at the top activating examples of [...]

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Outline:

(00:59) What does it take to explain a neuron?

(03:15) Simulation scores tell us whether we explained a neuron

(05:41) Beyond understanding neurons

The original text contained 7 footnotes which were omitted from this narration.

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First published:

October 17th, 2026


Source:

https://www.lesswrong.com/posts/Dkh7pXKjoNWkkxQDz/neural-networks-are-not-already-clean

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Narrated by TYPE III AUDIO.