Reward Hacking by Reasoning Models & Loss of Control Scenarios w/ Jeffrey Ladish of Palisade Research, from FLI Podcast
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What is this episode about and who is the guest?
Hello, and welcome back to the cognitive revolution. Today I'm excited to share an episode of the Future of Life Institute Podcast, to which I've been a longtime subscriber and where I've been twice honored to appear as a guest, featuring a conversation between Jeffrey Laddish, Executive Director of Palisade Research, and host, Gus Docker. This crosspost came about as I was preparing to interview Jeffrey myself. I had reached out to Jeffrey after seeing Palisade's recent work on reward hacking by reasoning models, and even scheduled a time to record. But Gus beat me to it, and after listening to this conversation, I thought that I could save Jeffrey some valuable time by crossposting instead, and really appreciate Gus for allowing me to do that.
Palisade Research studies dangerous capabilities of AI systems, particularly focusing on loss of control scenarios. And as you'll hear, Jeffrey, who previously helped build the information security program at Anthropic, is an AI industry insider who believes that we're rapidly approaching the time when AIs will be sufficiently capable of hacking, deception, and long term planning so as to present clear and present dangers. He also reports that his friends working in research at Frontier Labs often say that while they're increasingly fearful of the overall trajectory of AI development, they ultimately feel that their hand is forced by competitive pressures to keep moving forward. In this conversation, Jeffrey describes two broad ways that humans could conceivably lose control.
Acute crises in which superhuman AI systems actively work against human interests, and slower moving scenarios where society gradually but irreversibly shifts more and more decision making responsibility to AI systems. He also goes into detail about their recent research into reward hacking by reasoning models in the context of chess games. As we've seen repeatedly now, models trained with reinforcement learning are more prone to a variety of bad behaviors. And as a recent paper by OpenAI showed, this is not an easy problem to solve. Toward the end, Jeffrey outlines what he thinks we should do about all this, advocating for greater coordination among AI labs, more transparency about capabilities, and potentially restricting further development of the most dangerous capabilities while continuing beneficial research and deployment.
Now, that probably won't happen, barring a sufficiently shocking and damaging incident, but research of the sort that Jeffrey and team are doing is becoming more important all the time. So I'll definitely be following their latest results and look forward to discussing in a future episode with Jeffrey as well. As always, if you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post a review on Apple Podcasts or Spotify, or share any feedback or topic and guest suggestions that you have, either via our website, cognitive evolution.ai, or by DMing me on your favorite social network. Now, I hope you enjoy this conversation about AI reward hacking research and the big picture of AI risk and strategy.
with Jeffrey Laddish of Palisade Research from the Future of Life Institute podcast.
Welcome to the Future of Life Institute podcast. My name is Gus Docker, and I'm here with Jeffrey Laddish from Palisade Research. Jeffrey, welcome to the podcast. Hey guys, it's great to be here. Fantastic. Maybe start a bit by telling us about what it is you do at Palisade.
Yeah, happy to. So we are trying to study risks from emerging AI systems. And in particular, we are trying to better understand loss of control risks. And so, you know, this both looks like trying to understand, you know, sort of what are some of the strategic capabilities that are emerging in AI systems? You know, where might they act out in ways that will be hard to control? Um, and then we are trying to sort of present things that we think we know about this to the public. public to policymakers to help people better understand what is this weird situation we're in.
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Chapters
8 chapters
1
What is this episode about and who is the guest?
0:00–4:10
2
How fast is AI progress accelerating and what does that mean for safety?
4:10–7:57
3
What are the possible ways we could lose control of powerful AI systems?
7:57–32:04
4
Why might AI systems eventually turn against human interests?
32:04–36:51
5
How did reasoning models manage to hack a chess environment and what does it show?
36:51–57:53
6
Can we create honest AI systems and what challenges does that involve?
57:53–1:07:21
7
How good is cybersecurity at leading AI companies like OpenAI and Anthropic?
1:07:21–1:18:46
8
What should we do right now to reduce the risk of losing control of AI?
1:18:46–1:25:57
Speakers
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