[LIVE] Anthropic Distillation & How Models Cheat (SWE-Bench Dead) | Nathan Lambert & Sebastian Raschka
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What is the main focus of today’s live episode?
Okay, we're live. We have one person. Um people will start trickling in. Thanks for coming to Sale Live number six. This is a very exciting one. I think we have a I mean, the topics are always fun with these. It's whatever is the topic of the day on our little rat racing minds trying to keep up with AI. But we're welcoming the latest writer that is joining the sale coalition. So I think this just means more content for sale. I think I've been a fan of Swix and a friend for a while at this point. So I'm very happy to have his content join this. And I think you've been doing great stuff recently and continuing to evolve this. So thank you, Tier. Welcome to the team. I just this is like my friends and and colleagues in the AI media space and it's just great to be able to support people and keep that network closer.
So
Well yeah. Um thanks for uh I just wanted to say uh uh thanks for joining us. It's uh really a pleasure to have you on here, uh Sean or Swix. Um so yeah, uh awesome. Uh I just uh coincidentally listened to your podcast about the Sper benchmark. Um so yeah, awesome to, you know, small world, awesome to have you here.
Yeah, th thanks for having me and uh yeah, it's just glad to be on in chat. Uh I've never ever done one of these Substack live things, so I'm curious how it works. I always think about Substack 'cause I can use later platform. Uh but they wanna go multimedia.
I think the live thing before we get to technical content is actually good because it gives it a different edge. It's just like a little bit sharper when you know you're live. I think we've all done a lot of podcasts, even podcasts that are unedited and put there's later. But I think the live thing is a different element that can be tapped into nicely. So I don't know. Why don't we type why don't we just dive into it? We're gonna start with. distillation, I put I put how models cheat in the top so we can talk about benchmarks. I think Athropic posted this pretty spicy blog post this week. I think. It was essentially detailing how they found distributed distillation quote unquote attacks on their surfaces from prominent Chinese labs.
And I'm very unsurprised with Anthropic calling it an attack. I think that that fits with a lot of their Branding. Okay, nice. Screen share. This is what we mean. Sean is Sean Swix is such a pro. And it's like and the screen share fee was only dropped a few days ago. But essentially It's Anthropic is detailing how they found distributed accounts across multiple tr Chinese labs building shade of our LOMs and described what they were doing and why Enthropic is concerned about this in their worldview of like AI geopolitics. And I think it's very interesting because I'm of the opinion that the Chinese labs like obviously should do this. They're in a massive GPU shortage and using APIs is way easier than generating synthetic data on their own.
And I think that's why If
I may interrupt you here, maybe we should just for the general audience uh just define distillation before we maybe dive into the details. Um yeah, so distillation that's like a broader concept. Uh it's not like a new concept that came up with LLMs. It's like an older concept in machine learning in general. And uh distillation essentially is the the idea is that you're taking a larger model and uh trying Train it on the outputs. Sorry, you you have a larger model, let it generate outputs, and train a smaller model on these outputs of the larger model. And the idea is that you can train the smaller model more efficiently using that larger model. And originally, I think yeah, you just brought up the paper here.
Originally what you would do is you would train on the logits. So old school machine learning people might remember from deep neural networks like the logits, the outputs of the last layer that you usually work for uh with them to compute the loss function across entropy term. And you would train on on this signal. And um nowadays in the context of LLMs, it's a bit more loose. So it does not have to be these logits that you train on.
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Chapters
6 chapters
1
What is the main focus of today’s live episode?
0:00–12:12
2
How do the hosts define model distillation and why does it matter?
12:12–21:30
3
What are Anthropic’s “distributed distillation attacks” and how were they discovered?
21:30–24:32
4
How can companies detect if a model is being used for a distillation attack versus normal evaluation?
24:32–29:13
5
What are the current terms‑of‑service restrictions on using LLM outputs for training?
29:13–48:15
6
Why is the SWE‑Bench benchmark controversial and what are its verified and pro versions?
48:15–51:45
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